Forex Predição Svm

Suporte Vector Machines for Regression O método Support Vector também pode ser aplicado ao caso de regressão, mantendo todas as principais características que caracterizam o algoritmo de margem máxima: uma função não linear é aprendida por uma máquina de aprendizado linear em um espaço de recurso induzido pelo kernel Enquanto a capacidade do sistema é controlada por um parâmetro que não depende da dimensionalidade do espaço. Cristianini e Shawe-Taylor (2000) Em SVM a idéia básica é mapear os dados x em um espaço F de grande dimensão através de um mapeamento não-linear. E fazer a regressão linear neste espaço (ver Boser et al. (1992) Vapnik (1995)). A simulação mostra que os especialistas em SVMs conseguem uma melhoria significativa no desempenho de generalização em comparação com os modelos SVMs únicos. Além disso, os especialistas em SVM também convergem mais rapidamente e usam menos vetores de suporte. Cao (2002) GAO, J. B. S. R. GUNN e C. J. HARRIS, Método de campo médio para a regressão de máquina de vetor de suporte Este artigo trata de dois assuntos. Primeiro, mostraremos como o problema de regressão da máquina de vetores de suporte (SVM) pode ser resolvido como a máxima previsão a posteriori na estrutura bayesiana. A segunda parte descreve uma técnica de aproximação que é útil na realização de cálculos para SVMs com base no algoritmo de campo médio que foi originalmente proposto na Física Estatística de sistemas desordenados. Uma vantagem é que ele lida com médias posteriores para o processo gaussiano que não são analiticamente tratáveis. Gao, Gunn e Harris (2002) GUNN, S. Máquinas Vector de Suporte para Classificação e Regressão. ISIS Technical Report, 1998. Citado por 164 HARLAND, Zac, Usando Máquinas de Suporte Vectorial para Comercializar Alumínio no LME. Este artigo descreve e avalia o uso da regressão do vetor de suporte para negociar o contrato de futuros de alumínio de três meses na London Metal Exchange, no período de junho de 1987 a novembro de 1999. A Vector Vector Machine é um método de aprendizagem mecânica para classificação e regressão e é rápida Substituindo redes neurais como a ferramenta de escolha para a predição e reconhecimento de padrões tarefas, principalmente devido à sua capacidade de generalizar bem em dados invisíveis. O algoritmo é baseado em idéias derivadas da teoria da aprendizagem estatística e pode ser compreendido intuitivamente dentro de uma estrutura geométrica. Neste artigo, utilizamos a regressão de vetor de suporte para desenvolver um número de submodelos de negociação que, quando combinados, resultam em um modelo final que exibe retornos acima da média dos dados de amostra, fornecendo assim alguma evidência de que o preço de futuros de alumínio é menos eficiente. Se essas ineficiências continuarão no futuro é desconhecido. Harland HONG, Hun Dug, Changha HWANG, suporte de vetores de máquinas de regressão fuzzy máquina de vetores de suporte (SVM) tem sido muito bem sucedida no reconhecimento de padrões e função estimationproblems. Neste artigo, apresentamos o uso da SVM para modelos de regressão linear e não linear linear e fuzzy multivariada. Usando a idéia básica subjacente SVM para regressões fuzzy multivariada dá eficiência computacional de obter soluções. Hong e Hwang M220LLER, K.-R. Et ai. Usando máquinas de vetores de suporte para séries temporais As máquinas de vetor de suporte de previsão são usadas para previsão de séries temporais e comparadas com redes de função de base radial. Utilizamos duas funções de custo diferentes para Vetores de Suporte: treinamento com (i) perda epsilon insensível e (ii) função de perda robusta de Hubers e discutimos como escolher os parâmetros de regularização nesses modelos. Duas aplicações são consideradas: dados de (a) um sistema Mackey-Glass ruidoso (ruído normal e uniforme) e (b) o Concurso de Série Temporal de Santa Fe (conjunto D). Em ambos os casos, o Support Vector Machines apresenta um excelente desempenho. No caso (b), a abordagem do Vector de Apoio melhora o resultado mais conhecido no benchmark por 29.Muller et al. (2000) PONTIL, Massimiliano, Sayan MUKHERJEE e Federico GIROSI, sobre o modelo de ruído de suporte à regressão de máquinas de vetores Pontil, Mukherjee e Girosi (1998) SMOLA, Alex J. e Bernhard SCH214LKOPF, um tutorial sobre suporte à regressão vetorial Smola e Scholkopf ) Citado por 309Support Vector Machines: Aplicações Financeiras Listadas em ordem de citações por ano, mais altas no topo. Última atualização em setembro de 2006. PANG, Bo, Lillian LEE e Shivakumar VAITHYANATHAN, 2002. Thumbs up Sentiment Classificação usando Técnicas de Aprendizagem de Máquinas. Em: EMNLP 02: Actas da Conferência ACL-02 sobre Métodos Empíricos no Processamento da Linguagem Natural - Volume 10. Páginas 79-86. Citado por 154 (36.66 anos) Resumo: Consideramos o problema de classificar documentos não por tópico, mas pelo sentimento geral, p. Determinar se uma revisão é positiva ou negativa. Usando críticas de filmes como dados, descobrimos que as técnicas padrão de aprendizado de máquinas superam definitivamente as linhas de base produzidas pelo homem. No entanto, os três métodos de aprendizagem de máquina que empregamos (Naive Bayes, classificação de entropia máxima e máquinas de vetores de suporte) não funcionam tão bem na classificação de sentimentos quanto na categorização tópica tradicional. Concluímos examinando os fatores que tornam o problema da classificação do sentimento mais desafiador. Descobriu que, usando revisões de filmes como dados, as técnicas padrão de aprendizado de máquinas superaram definitivamente as linhas de base produzidas por humanos. No entanto, eles também descobriram que os três métodos de aprendizagem de máquina que eles empregavam (Naive Bayes, classificação de entropia máxima e máquinas de vetores de suporte) não funcionaram tão bem na classificação de sentimentos quanto na categorização tópica tradicional. VAN GESTEL, Tony, et ai. . 2001. Séries Temporais Financeiras Previsão Utilizando Mínimos Quadrados Suporte Vector Machines Dentro do Quadro de Evidência. Transações IEEE em redes neurais. Volume 12, Número 4, Julho de 2001, Páginas 809-821. A estrutura de evidências bayesianas é aplicada neste trabalho à regressão da máquina de vetores de suporte de mínimos quadrados (LS-SVM), a fim de inferir modelos não-lineares para prever uma série de tempo financeiro e a volatilidade associada. No primeiro nível de inferência, um quadro estatístico está relacionado com a formulação LS-SVM que permite incluir a volatilidade variável no tempo do mercado através de uma escolha adequada de vários hiperparâmetros. Os hiperparâmetros do modelo são inferidos no segundo nível de inferência. Os hiperparâmetros inferidos, relacionados à volatilidade, são utilizados para construir um modelo de volatilidade dentro da estrutura de evidências. A comparação de modelos é realizada no terceiro nível de inferência, a fim de ajustar automaticamente os parâmetros da função do kernel e selecionar as entradas relevantes. A formulação LS-SVM permite derivar expressões analíticas no espaço de características e expressões práticas são obtidas no espaço duplo substituindo o produto interno pela função de núcleo relacionada usando o teorema de Mercers. Os prognósticos de previsão um passo adiante obtidos na previsão da taxa semanal de T-day de 90 dias e dos preços de fechamento diários do DAX30 mostram que podem ser feitas previsões significativas de sinal de amostra com relação ao teste de Pesaran-Timmerman, o quadro de evidências bayesianas A regressão de máquina de vetor de suporte de mínimos quadrados (LS-SVM) para prever a taxa semanal de T-bill de 90 dias e os preços de fechamento diários de DAX30. TAY, Francis E. H. e Lijuan CAO, 2001. Aplicação de máquinas de vetores de suporte em previsões de séries temporais financeiras. Omega: O Jornal Internacional de Ciência de Gestão. Volume 29, Edição 4, Agosto de 2001, Páginas 309-317. Este artigo trata da aplicação de uma nova técnica de rede neural, a máquina de suporte ao vetor (SVM), na previsão de séries temporais financeiras. O objetivo deste artigo é examinar a viabilidade da SVM na previsão de séries temporais financeiras comparando-a com uma rede neural de multi-camada de retroprovaç~ao (BP). Cinco contratos de futuros reais que são compilados a partir do Mercado Mercantil de Chicago são usados ​​como os conjuntos de dados. A experiência mostra que o SVM supera a rede neural de BP com base nos critérios de erro quadrático médio normalizado (NEM), erro absoluto médio (MAE), simetria direcional (DS) e simetria direcional ponderada (WDS). Como não existe uma forma estruturada de escolher os parâmetros livres das SVMs, a variabilidade no desempenho com relação aos parâmetros livres é investigada neste estudo. A análise dos resultados experimentais provou que é vantajoso aplicar SVMs para prever séries de tempo financeiro. Descobriu que um SVM superou uma rede neural de multi-camada de back-propagation (BP) em cinco contratos de futuros reais do Chicago Mercantile Market. TAY, Francis E. H. e L. J. CAO, 2002. Máquinas modulares de vetor de suporte em previsões de séries temporais financeiras. Neurocomputing. Volume 48, Edições 1-4, Outubro de 2002, Páginas 847-861. Este artigo propõe uma versão modificada de máquinas de vetores de suporte, denominada C-ascending support vector machine, para modelar séries temporais financeiras não-estacionárias. As máquinas de vectores de apoio em C são obtidas por uma simples modificação da função de risco regularizada em máquinas de suporte de vectores, pelo que os erros 949 insensíveis recentes são penalizados mais fortemente do que os erros distantes 949-insensíveis. Esse procedimento baseia-se no conhecimento prévio de que, na série de tempo financeiro não-estacionário, a dependência entre variáveis ​​de entrada e variável de saída varia gradualmente ao longo do tempo, especificamente, os dados passados ​​recentes poderiam fornecer informações mais importantes do que os dados do passado distante. No experimento, as máquinas de vetores de suporte C-ascending são testadas usando três futuros reais coletados do Chicago Mercantile Market. Mostra-se que as máquinas de vetores de suporte em C com os dados de amostra realmente ordenados projetam consistentemente melhor do que as máquinas de vetores de suporte padrão, com o pior desempenho quando se usam os dados de amostra ordenados inversamente. Além disso, as máquinas de vectores de apoio em C utilizam menos vectores de suporte do que as máquinas de suporte de suporte padrão, resultando numa representação mais dispersa de máquinas de vector de suporte em C, desenvolvidas em solução, que penalizam erros recentes sensíveis a 949 são mais pesados ​​do que distantes 949-erros insensíveis, e descobriu que eles prevêem melhor do que SVMs padrão em três futuros reais coletados do Mercado Mercantil de Chicago. HUANG, Zan, et ai. . 2004. Análise de rating de crédito com máquinas de vetores de suporte e redes neurais: um estudo comparativo de mercado. Sistemas de Suporte à Decisão . Volume 37, Edição 4 (Setembro 2004), Páginas 543-558. Citado por 21 (9.55 anos) Resumo: A análise de rating de crédito corporativo tem atraído muitos interesses de pesquisa na literatura. Estudos recentes demonstraram que os métodos de Inteligência Artificial (IA) obtiveram melhor desempenho do que os métodos estatísticos tradicionais. Este artigo introduz uma técnica relativamente nova de aprendizado de máquina, máquinas de vetores de suporte (SVM), para o problema na tentativa de fornecer um modelo com melhor poder explicativo. Utilizamos a rede neuronal de retropropagação (BNN) como referência e obtivemos uma precisão de previsão de cerca de 80 para os métodos BNN e SVM para os mercados dos Estados Unidos e Taiwan. No entanto, observou-se apenas uma ligeira melhoria da SVM. Outra direção da pesquisa é melhorar a interpretabilidade dos modelos baseados em AI. Nós aplicamos resultados de pesquisas recentes na interpretação de modelos de redes neurais e obtivemos a importância relativa das variáveis ​​financeiras de entrada dos modelos de redes neurais. Com base nesses resultados, realizamos uma análise comparativa de mercado sobre as diferenças de fatores determinantes nos mercados dos Estados Unidos e Taiwan. Aplicamos as redes neurais de backpropagation e SVMs à previsão de classificação de crédito corporativo para os mercados dos Estados Unidos e Taiwan e verificamos que os resultados eram comparáveis (Ambos foram superiores à regressão logística), com o SVM ligeiramente melhor. CAO, Lijuan, 2003. Suporte máquinas de vetores especialistas para a previsão de séries temporais. Neurocomputing. Volume 51, Abril de 2003, páginas 321-339. Citado por 29 (9.08 anos) Resumo: Este artigo propõe o uso de máquinas de vetores de suporte (SVMs) para a previsão de séries temporais. Os especialistas em SVM generalizados possuem uma arquitetura de rede neural de dois estágios. Na primeira fase, o mapa de características auto-organizado (SOM) é usado como um algoritmo de agrupamento para dividir todo o espaço de entrada em várias regiões disjuntas. Uma arquitetura estruturada em árvore é adotada na partição para evitar o problema de predeterminar o número de regiões particionadas. Então, na segunda fase, vários SVMs, também chamados especialistas em SVM, que melhor se encaixam regiões particionadas são construídos encontrando a função de kernel mais adequada e os parâmetros livres ótimos de SVMs. Os dados de manchas solares, os conjuntos de dados A, C e D de Santa Fe e os dois conjuntos de dados de construção são avaliados no experimento. A simulação mostra que os especialistas em SVMs melhoram significativamente o desempenho de generalização em comparação com os modelos SVMs únicos. Além disso, os especialistas em SVMs também convergem mais rapidamente e usam menos vetores de suporte. Demonstrou que seu método de especialistas em SVM alcançou melhora significativa acima dos modelos de SVMs únicos quando aplicados ao conjunto de dados C de Santa Fé (taxas de câmbio de alta freqüência entre o franco suíço e o Dólar americano). KIM, Kyoung-jae, 2003. Previsão financeira de séries temporais usando máquinas de vetores de suporte. Neurocomputing. Volume 55, Edições 1-2 (Setembro 2003), Páginas 307-319. Máquinas de vetores de suporte (SVMs) são métodos promissores para a previsão de séries temporais financeiras porque utilizam uma função de risco que consiste no erro empírico e um termo regularizado que é derivado do princípio de minimização do risco estrutural . Este estudo aplica SVM à previsão do índice de preços das ações. Além disso, este estudo examina a viabilidade da aplicação da SVM na previsão financeira, comparando-a com redes neurais de retro-propagação e raciocínio baseado em casos. Os resultados experimentais mostram que a SVM oferece uma alternativa promissora para a previsão do mercado de ações. Descobriu que as SVMs superaram as redes neurais de retro-propagação e o raciocínio baseado em casos quando usadas para prever o índice diário das ações da Coréia (KOSPI). SHIN Kyung-Shik, Taik Soo LEE e Hyun-jung KIM, 2005. Uma aplicação de máquinas de vetores de apoio no modelo de previsão de falências. Sistemas especialistas com aplicações. Volume 28, Edição 1, Janeiro de 2005, Páginas 127-135. Citado por 8 (6,6 anos) Resumo: Este estudo investiga a eficácia da aplicação de máquinas de suporte vetorial (SVM) ao problema de previsão de falências. Embora seja um fato bem conhecido que a rede neuronal de retropropagação (BPN) funciona bem em tarefas de reconhecimento de padrões, o método tem algumas limitações em que é uma arte encontrar uma estrutura de modelo apropriada e solução ideal. Além disso, é necessário carregar o maior número de treinamento possível na rede para pesquisar os pesos da rede. Por outro lado, uma vez que SVM captura características geométricas de espaço de recurso sem derivar pesos de redes a partir dos dados de treinamento, é capaz de extrair a solução ideal com o pequeno tamanho de conjunto de treinamento. Neste estudo, mostramos que o classificador proposto da abordagem SVM supera BPN ao problema da previsão de falências corporativas. Os resultados demonstram que a precisão eo desempenho de generalização de SVM é melhor do que o do BPN à medida que o tamanho do conjunto de treinamento se torna menor. Também examinamos o efeito da variabilidade no desempenho em relação a vários valores de parâmetros na SVM. Além disso, investigamos e resumimos os vários pontos superiores do algoritmo SVM em comparação com o BPN. Demonstrou que as SVMs funcionam melhor do que as redes neurais de retroprovaç~ao quando aplicadas à previsão de falência corporativa. CAO, L. J. e Francis E. H. TAY, 2003. Suporta Vector Machine com parâmetros adaptativos em Financial Time Series Forecasting. Transações IEEE em redes neurais. Volume 14, Edição 6, Novembro de 2003, Páginas 1506-1518. Um novo tipo de máquina de aprendizagem chamada máquina de suporte de vetores (SVM) tem recebido crescente interesse em áreas que vão desde a sua aplicação original em reconhecimento de padrões até outras aplicações, como estimativa de regressão devido ao seu notável desempenho de generalização . Este artigo trata da aplicação de SVM na previsão de séries temporais financeiras. A viabilidade da aplicação de SVM na previsão financeira é examinada pela primeira vez comparando-a com a rede neural de back-propagação multicamada (BP) e a rede neural de função de base radial regularizada (RBF). A variabilidade no desempenho da SVM em relação aos parâmetros livres é investigada experimentalmente. Parâmetros adaptativos são então propostos incorporando a não-estacionariedade de séries de tempo financeiro em SVM. Cinco contratos de futuros reais recolhidos do Mercado Mercantil de Chicago são usados ​​como conjuntos de dados. A simulação mostra que entre os três métodos, SVM supera a rede neural de BP na previsão financeira, e há desempenho de generalização comparável entre SVM ea rede neural regularizada RBF. Além disso, os parâmetros livres de SVM têm um grande efeito no desempenho de generalização. SVM com parâmetros adaptativos podem tanto alcançar maior desempenho de generalização e usar menos vetores de suporte do que o SVM padrão na previsão financeira. Utilizou-se um SVM, uma rede neural de back-propagation (BP) multicamada e uma rede neural de função radial regularizada (RBF) para prever Cinco contratos de futuros reais recolhidos a partir do Mercado Mercantil de Chicago. Os resultados mostraram que a SVM e a rede neural regularizada de RBF eram comparáveis ​​e ambas superaram a rede neural de PB. CAO, Lijuan e Francis E. H. TAY, 2001. Previsão Financeira Usando Máquinas de Suporte. Aplicações de amplificação de computação neural. Volume 10, Número 2 (Maio de 2001), Páginas 184-192. O uso de Máquinas de Suporte Vector (SVMs) é estudado na previsão financeira, comparando-o com um perceptron de várias camadas treinado pelo algoritmo de Back Propagation (BP). As SVMs previu melhor do que a BP com base nos critérios de erro de quadrado médio normalizado (NMSE), erro médio absoluto (MAE), simetria direcional (DS), tendência de Correct Up (CP) e tendência de Correct Down (CD). O índice de preços diários SampP 500 é usado como conjunto de dados. Como não existe uma maneira estruturada de escolher os parâmetros livres das SVMs, o erro de generalização com relação aos parâmetros livres de SVMs é investigado nesta experiência. Conforme ilustrado no experimento, eles têm pouco impacto na solução. A análise dos resultados experimentais demonstra que é vantajoso aplicar SVMs para prever a série de tempo financeiro. Descobriu que os SVMs previam melhor o índice de preços diários do SampP 500 do que um perceptron multi-camada treinado pelo algoritmo Back Propagation (BP). MIN, Jae H. e Young-Chan LEE, 2005. Previsão de falência usando máquina de vetores de suporte com a melhor escolha de parâmetros de função do kernel. Sistemas especialistas com aplicações. Volume 28, Número 4, Maio de 2005, Páginas 603-614. A previsão de falências tem atraído muitos interesses de pesquisa na literatura anterior, e estudos recentes mostraram que as técnicas de aprendizado mecânico alcançaram melhor desempenho do que as estatísticas tradicionais. Este artigo aplica máquinas de vetores de suporte (SVMs) ao problema de previsão de falências numa tentativa de sugerir um novo modelo com melhor poder explicativo e estabilidade. Para isso, usamos uma técnica de busca por grade usando validação cruzada de 5 vezes para descobrir os valores de parâmetros ótimos da função do kernel da SVM. Além disso, para avaliar a precisão de predição de SVM, comparamos seu desempenho com os de análise discriminante múltipla (MDA), análise de regressão logística (Logit) e redes neurais de retropropagação de três camadas totalmente conectadas (BPNs). Os resultados da experiência demonstram que a SVM supera os outros métodos. Descobriram que, quando aplicados à previsão de falência, as SVMs superaram a análise discriminante múltipla (MDA), a análise de regressão logística (Logit) e as redes neurais de retroprodução (BPNs) totalmente interligadas de três camadas. ABRAHAM, Ajith, Ninan Sajith PHILIP e P. SARATCHANDRAN, 2003. Modelagem do comportamento caótico de índices de ações usando paradigmas inteligentes. Neural, Amplificador Paralelo Computações Científicas. Volume 11, páginas 143-160. Citado por 10 (4,55 anos) Resumo: O uso de sistemas inteligentes para predições do mercado de ações tem sido amplamente estabelecido. Neste artigo, investigamos como o comportamento aparentemente caótico dos mercados de ações poderia ser bem representado usando vários paradigmas conexionistas e técnicas de soft computing. Para demonstrar as diferentes técnicas, consideramos o índice Nasdaq-100 da Nasdaq Stock Market SM eo índice de ações SP CNX NIFTY. Foram analisados ​​os valores dos índices principais do Nasdaq 100 de 7 anos e os valores do índice NIFTY de 4 anos. Este artigo investiga o desenvolvimento de uma técnica confiável e eficiente para modelar o comportamento aparentemente caótico dos mercados de ações. Nós consideramos uma rede neural artificial treinada usando o algoritmo de Levenberg-Marquardt, a máquina do vetor da sustentação (SVM), o modelo neurofuzzy de Takagi-Sugeno e uma rede neural do impulso da diferença (DBNN). Este artigo explica brevemente como os diferentes paradigmas conexionistas podem ser formulados usando diferentes métodos de aprendizagem e, em seguida, investiga se eles podem fornecer o nível requerido de desempenho, que são suficientemente bom e robusto para fornecer um modelo de previsão confiável para os índices do mercado de ações. Os resultados experimentais revelam que todos os paradigmas conexionistas considerados poderiam representar com precisão o comportamento dos índices de ações. Aplicaram-se quatro técnicas diferentes, uma rede neural artificial treinada usando o algoritmo de Levenberg-Marquardt, uma máquina de vetores de suporte, uma rede neuronal estimuladora de diferença e um Takagi-Sugeno Sistema de inferência fuzzy aprendido usando um algoritmo de rede neural (modelo neuro-fuzzy) para a previsão do índice Nasdaq-100 da Nasdaq Stock Market e do índice de ações SP CNX NIFTY. Nenhuma técnica foi claramente superior, mas absurdamente, eles tentam prever o valor absoluto dos índices, ao invés de usar log retorna. YANG, Haiqin, Laiwan CHAN e Irwin KING, 2002. Regressão da máquina do vetor da sustentação para a predição volátil do mercado conservado em estoque. Em Engenharia de Dados Inteligente e Aprendizagem Automatizada: IDEAL 2002. Editado por Hujun Yin, et ai. . Páginas 391-396, Springer. Recentemente, a Regressão Vectorial de Apoio (SVR) foi introduzida para resolver problemas de regressão e predição. Neste artigo, aplicamos SVR às tarefas de previsão financeira. Em particular, os dados financeiros são normalmente ruidosos e o risco associado é variável no tempo. Portanto, nosso modelo SVR é uma extensão do SVR padrão que incorpora a adaptação de margens. Ao variar as margens da SVR, poderíamos refletir a mudança na volatilidade dos dados financeiros. Além disso, analisamos o efeito das margens assimétricas de forma a permitir a redução do risco de queda. Nossos resultados experimentais mostram que o uso do desvio padrão para calcular uma margem variável dá um bom resultado preditivo na previsão do Índice Hang Seng. A variação das margens na regressão SVM para refletir a mudança na volatilidade dos dados financeiros e também analisou Efeito de margens assimétricas de modo a permitir a redução do risco de queda. A primeira abordagem produziu o erro total mais baixo ao prever o preço de fechamento diário do Hong Kongs Hang Seng Index (HSI). HUANG, W. Y. NAKAMORI e S. Y. WANG, 2005. Previsão da direção do movimento do mercado de ações com a máquina do vetor do apoio. Computadores Investigação Operacional. Volume 32, Edição 10, Páginas 2513-2522. (SVM) é um tipo muito específico de algoritmos de aprendizagem caracterizados pelo controle de capacidade da função de decisão, pelo uso das funções do kernel e pela dispersão da solução. Neste artigo, investigamos a previsibilidade da direção do movimento financeiro com SVM, previu a direção de movimento semanal do índice NIKKEI 225. Para avaliar a capacidade de previsão da SVM, comparamos seu desempenho com os da Análise Discriminante Linear, Análise Discriminante Quadrática e Redes Neurais de Backpropagação Elman. Os resultados da experiência mostram que a SVM supera os outros métodos de classificação. Além disso, propomos um modelo de combinação integrando SVM com os outros métodos de classificação. O modelo de combinação tem o melhor desempenho entre todos os métodos de previsão, comparando a capacidade de SVMs, Análise Discriminante Linear, Quadratic Discriminant Analysis e Elman Backpropagation Neural Networks para prever a direção de movimento semanal do índice NIKKEI 225 e verificou que o SVM superou todos os outros métodos de classificação . Melhor ainda era uma combinação ponderada dos modelos. TRAFALIS, Theodore B. e Huseyin INCE, 2000. Suporte Vector Machine para Regressão e Aplicações para Previsão Financeira. In: IJCNN 2000: Procedimentos da IEEE-INNS-ENNS Conferência Internacional Conjunta sobre Redes Neurais: Volume 6 editado por Shun-Ichi Amari, et al. . Página 6348, IEEE Computer Society. O objetivo principal deste trabalho é comparar a máquina de vetores de suporte (SVM) desenvolvida por Vapnik com outras técnicas como Backpropagation e Radial Basis Function (RBF) para aplicações de previsão financeira. A teoria do algoritmo SVM é baseada na teoria da aprendizagem estatística. O treinamento de SVMs leva a um problema de programação quadrática (QP). Os resultados preliminares computacionais para a predição do preço das ações também são apresentados com SVMs com Redes de Backpropagation e Radial Basis Function (RBF), prevendo os preços diários de ações da IBM, Yahoo e America Online. Estranhamente, usando o SVM para a regressão eles forwent um conjunto de validação, definir epsilon para zero, C fixo e repetiu a experiência para várias configurações fixas do kernel parâmetro, sigma, dando origem a vários resultados. CAO, Lijuan e Qingming GU, 2002. Máquinas dinâmicas do vetor do apoio para a previsão não-estacionária da série de tempo. Análise Inteligente de Dados. Volume 6, Número 1, Páginas 67-83. Este artigo propõe uma versão modificada de máquinas de vetores de suporte (SVMs), denominadas máquinas de vetores de suporte dinâmico (DSVMs), para modelar séries temporais não-estacionárias. As DSVMs são obtidas incorporando o conhecimento do domínio do problema - não-estacionaridade de séries temporais em SVMs. Ao contrário dos SVMs padrão que usam valores fixos da constante de regularização e do tamanho do tubo em todos os pontos de dados de treinamento, as DSVMs usam uma constante de regularização exponencialmente crescente e um tamanho de tubo exponencialmente decrescente para lidar com as mudanças estruturais nos dados. A constante de regularização dinâmica eo tamanho do tubo baseiam-se no conhecimento prévio de que os pontos de dados recentes não estacionários podem fornecer informações mais importantes do que pontos de dados distantes. Na experiência, as DSVMs são avaliadas usando conjuntos de dados simulados e reais. A simulação mostra que as DSVMs generalizam melhor do que as SVMs padrão na previsão de séries temporais não-estacionárias. Uma outra vantagem desta modificação é que as DSVMs usam menos vetores de suporte, resultando em uma representação mais esparsa da solução. Incorporam o conhecimento prévio de que as séries temporais financeiras não são estacionárias em suas máquinas de suporte dinâmico de suporte (DSVMs) e usam uma constante de regularização exponencialmente crescente e Um tamanho de tubo exponencial diminuindo para lidar com mudanças estruturais nos dados na suposição de que pontos de dados recentes poderiam fornecer informações mais importantes do que pontos de dados distantes. Eles concluem que as DSVMs generalizam melhor do que as SVMs padrão na previsão de séries temporais não-estacionárias, enquanto também usam menos vetores de suporte, resultando em uma menor representação da solução. TAY, Francis E. H. e L. J. CAO, 2002. 949-Máquinas de Vector de Suporte Descendente para Previsões de Séries Temporais Financeiras. Neural Processing Letters 15 (2): 179-195. Este artigo propõe uma versão modificada de máquinas de vetores de suporte (SVMs), chamadas 949-descendentes de máquinas de suporte vetorial (949-DSVMs), para modelar séries temporais financeiras não-estacionárias. Os 949-DSVMs são obtidos incorporando o problema do domínio do conhecimento 8211 não estacionário de séries de tempo financeiro em SVMs. Ao contrário dos SVMs padrão que usam um tubo constante em todos os pontos de dados de treinamento, os 949-DSVMs usam um tubo adaptativo para lidar com as mudanças de estrutura nos dados. A experiência mostra que os 949-DSVMs generalizam melhor do que os SVMs padrão na previsão Séries temporárias financeiras não-estacionárias. Outra vantagem desta modificação é que os 949-DSVMs convergem para menos vetores de suporte, resultando em uma representação mais esparsa da solução. Incorporou o conhecimento do domínio do problema de não estacionariedade de séries temporais financeiras em SVMs usando um tubo adaptativo em seus chamados Epsilon-descendentes máquinas de suporte vetorial (epsilon-DSVMs). O experimento mostrou que as epsilon-DSVMs generalizam melhor do que as SVMs padrão na previsão de séries temporais financeiras não-estacionárias e também convergem para menos vetores de suporte, resultando em uma menor representação da solução. DEBNATH, Sandip e C. Lee GILES, 2005. Um Modelo Baseado em Aprendizagem para a Extração de Headline de Artigos de Notícias para Encontrar Sentenças Explicativas para Eventos. In: K-CAP 821705: Actas da 3ª Conferência Internacional sobre a Captação do Conhecimento. Páginas 189-190. Cited by 2 (1.67year) Resumo: A informação sobre os metadados desempenha um papel crucial no aumento da eficiência e arquivamento da organização de documentos. Os metadados de notícias incluem DateLine. ByLine. HeadLine e muitos outros. Descobrimos que as informações do HeadLine são úteis para adivinhar o tema do artigo de notícias. Particularmente para artigos de notícia financeira, nós encontramos que HeadLine pode assim ser especialmente útil para localizar frases explicativas para todos os eventos principais tais como mudanças significativas em preços conservados em estoque. Neste artigo, exploramos uma abordagem de aprendizado baseada em vetor de suporte para extrair automaticamente os metadados do HeadLine. Descobrimos que a precisão de classificação de encontrar o HeadLine s melhora se DateLine s são identificados primeiro. Em seguida, usamos o HeadLine extraído para iniciar um padrão de correspondência de palavras-chave para encontrar as frases responsáveis ​​pelo tema da história. Usando este tema e um modelo de linguagem simples, é possível localizar quaisquer sentenças explicativas para qualquer mudança de preço significativa. Uma abordagem inovadora de extrair metadados de notícias HeadLines usando SVMs e usá-los para encontrar temas de história para obter uma explicação baseada em sentença para um estoque mudança de preço. Van GESTEL, Tony, et ai. . 2003. Uma aproximação da máquina do vetor do apoio ao scoring do crédito. Bank en Financiewezen . Volume 2, March, Pages 73-82. Cited by 5 (1.56year) Abstract: Driven by the need to allocate capital in a profitable way and by the recently suggested Basel II regulations, financial institutions are being more and more obliged to build credit scoring models assessing the risk of default of their clients. Many techniques have been suggested to tackle this problem. Support Vector Machines (SVMs) is a promising new technique that has recently emanated from different domains such as applied statistics, neural networks and machine learning. In this paper, we experiment with least squares support vector machines (LS-SVMs), a recently modified version of SVMs, and report significantly better results when contrasted with the classical techniquespared four methodologies, Ordinary Least Squares (OLS), Ordinal Logistic Regression (OLR), the Multilayer Perceptron (MLP) and least squares support vector machines (LS-SVMs) when applied to credit scoring. The SVM methodology yielded significantly and consistently better results than the classical linear rating methods. FAN, Alan and Marimuthu PALANISWAMI, 2000. Selecting Bankruptcy Predictors Using a Support Vector Machine Approach. IJCNN 2000: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Volume 6 . edited by Shun-Ichi Amari et al. . page 6354. Cited by 9 (1.45year) Abstract: Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach. use SVMs to select bankruptcy predictors, and provide a comparative study. TAY, Francis Eng Hock and Li Juan CAO, 2001. Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map. Intelligent Data Analysis . Volume 5, Number 4, Pages 339-354. Cited by 7 (1.35year) Abstract: A two-stage neural network architecture constructed by combining Support Vector Machines (SVMs) with self-organizing feature map (SOM) is proposed for financial time series forecasting. In the first stage, SOM is used as a clustering algorithm to partition the whole input space into several disjoint regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM experts, that best fit each partitioned region are constructed by finding the most appropriate kernel function and the optimal learning parameters of SVMs. The Santa Fe exchange rate and five real futures contracts are used in the experiment. It is shown that the proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM modelbined SVMs with a self-organizing feature map (SOM) and tested the model on the Santa Fe exchange rate and five real futures contracts. They showed that their proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM model. SANSOM, D. C. T. DOWNS and T. K. SAHA, 2003. Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants. Journal of Electrical Electronics Engineering, Australia . Vol 22, No. 3, Pages 227-234. Cited by 5 (1.19year) Abstract: In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting utilising Neural Networks (NNs) and Support Vector Machines (SVM). Frequent regulatory changes in electricity markets and the quickly evolving market participant pricing (bidding) strategies cause efficient retraining to be crucial in maintaining the accuracy of electricity price forecasting models. The efficiency of NN and SVM retraining for price forecasting was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the period from September 1998 to December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting accuracies and so require less time to optimally train than NNs which can result in a solution at any of a large number of local minima. The SVM and NN forecasting accuracies were found to be very similar. evaluated utilising Neural Networks (NNs) and Support Vector Machines (SVM) for wholesale (spot) electricity price forecasting. The SVM required less time to optimally train than the NN, whilst the SVM and NN forecasting accuracies were found to be very similar. ABRAHAM, Ajith and Andy AUYEUNG, 2003. Integrating Ensemble of Intelligent Systems for Modeling Stock Indices. In: Proceedings of 7th International Work Conference on Artificial and Natural Neural Networks, Part II . Lecture Notes in Computer Science, Volume 2687, Jose Mira and Jose R. Alverez (Eds.), Springer Verlag, Germany, pp. 774-781, 2003. Cited by 3 (0.94year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock Market SM and the SampP CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered. considered an artificial neural network trained using Levenberg-Marquardt algorithm, a support vector machine, a Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network for predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. They concluded that an ensemble of the intelligent paradigms performed better than the individual methods. YANG, Haiqin, et al. . 2004. Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression. In: Neural Information Processing: Research and Development . edited by Jagath Chandana Rajapakse and Lipo Wang, Springer-Verlag. Cited by 2 (0.91year) Abstract: Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. The financial time series usually contains the characteristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have noticed that up margin and down margin would not necessary be the same, and we also observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adopt the momentum in the asymmetrical margins setting. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVMs for regression with non-fixed and asymmetrical margin settings, this time with momentum, to predict the Hang Seng Index and Dow Jones Industrial Average. PAI, Ping-Feng and Chih-Sheng LIN, 2005. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega . Volume 33, Issue 6, December 2005, Pages 497-505. Cited by 1 (0.84year) Abstract: Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising. proposed a hybrid ARIMA and support vector machine model for stock price forecasting, and results looked very promising. ABRAHAM, Ajith, et al. . 2002. Performance Analysis of Connectionist Paradigms for Modeling Chaotic Behavior of Stock Indices. In: Second international workshop on Intelligent systems design and application . edited by Ajith Abraham, et al. . pages 181--186. Cited by 3 (0.71year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketTM and the SP CNX NIFTY stock index. We analyzed 7 years Nasdaq 100 main index values and 4 years NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately. analysed the performance of an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN) when predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. YANG, Haiqin, I. KING and Laiwan CHAN, 2002. Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression. In: ICONIP 02. Proceedings of the 9th International Conference on Neural Information Processing. Volume 3 . edited by Lipo Wang, et al. . pages 1398--1402. Cited by 3 (0.71year) Abstract: Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVM regression with a non-fixed and asymmetrical margin, this time adapting the asymmetrical margins using momentum, and applied it to predicting the Hang Seng Index and the Dow Jones Industrial Average. GAVRISHCHAKA, Valeriy V. and Supriya B. GANGULI, 2003. Volatility forecasting from multiscale and high-dimensional market data. Neurocomputing . Volume 55, Issues 1-2 (September 2003), Pages 285-305. Cited by 2 (0.63year) Abstract: Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management. used SVMs for forecasting the volatility of foreign-exchange data. Their preliminary benchmark tests indicated that SVMs can perform significantly better than or comparable to both naive and GARCH(1,1) models. P201REZ-CRUZ, Fernando, Julio A. AFONSO-RODR205GUEZ and Javier GINER, 2003. Estimating GARCH models using support vector machines. Quantitative Finance . Volume 3, Number 3 (June 2003), Pages 163-172. Cited by 2 (0.63year) Abstract: Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods. used SVMs for regression to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns and showed that such estimates have a higher predicting ability than those obtained via common maximum likelihood (ML) methods. Van GESTEL, T. et al. . 2003. Bankruptcy prediction with least squares support vector machine classifiers. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings . pages 1-8. Cited by 2 (0.63year) Abstract: Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e. g. solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercers theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. used least squares support vector machine classifiers for predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. CAO, L. J. and W. K. CHONG, 2002. Feature extraction in support vector machine: a comparison of PCA, XPCA and ICA. ICONIP 02: Proceedings of the 9th International Conference on Neural Information Processing, Volume 2 . edited by Lipo Wang, et al. . pages 1001-1005. Cited by 2 (0.48year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, feature extraction is the first important step. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. PCA linearly transforms the original inputs into uncorrelated features. KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into statistically independent features. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. considered the application of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVMs for feature extraction. By examining the sunspot data and one real futures contract, they showed that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, they found that there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. CAO, L. J. and Francis E. H. TAY, 2000. Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents . edited by Kwong Sak Leung, Lai-Wan Chan and Helen Meng, pages 268-273. Cited by 3 (0.48year) Abstract: This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the Simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features. dealt with the application of saliency analysis to feature selection for SVMs. Five futures contracts were examined and they concluded that saliency analysis is effective in SVMs for identifying important features. ZHOU, Dianmin, Feng GAO and Xiaohong GUAN, 2004. Application of accurate online support vector regression in energy price forecast. WCICA 2004: Fifth World Congress on Intelligent Control and Automation, Volume 2 . pages 1838-1842. Cited by 1 (0.45year) Abstract: Energy price is the most important indicator in electricity markets and its characteristics are related to the market mechanism and the change versus the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability. In this paper, an accurate online support vector regression (AOSVR) method is applied to update the price forecasting model. Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets. applied an accurate online support vector regression (AOSVR) to forecasting the prices of the electric-power markets, results showed that it was effective. FAN, A. and M. PALANISWAMI, 2001. Stock selection using support vector machines. IJCNN01: International Joint Conference on Neural Networks, Volume 3 . Pages 1793-1798. Cited by 2 (0.38year) Abstract: We used the support vector machines (SVM) in a classification approach to beat the market. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208 over a five years period, significantly outperformed the benchmark of 71. We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25used SVMs for classification for stock selection on the Australian Stock Exchange and significantly outperformed the benchmark. Van GESTEL, Tony, et al. . 2000. Volatility Tube Support Vector Machines. Neural Network World . Vol. 10, number 1, pp. 287-297. Cited by 2 (0.32year) Abstract: In Support Vector Machines (SVM8217s), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term. By specifying a difference inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which arises in financial time series. The resulting Volatility Tube SVM8217s are applied on the 1-day ahead prediction of the DAX30 stock index. The influence of todays closing prices of the New York Stock Exchange on the prediction of tomorrow8217s DAX30 closing price is analyzed. developed the Volatility Tube SVM and applied it to 1-day ahead prediction of the DAX30 stock index, and significant positive out-of-sample results were obtained. CAO, Li Juan, Kok Seng CHUA and Lim Kian GUAN, 2003. Combining KPCA with support vector machine for time series forecasting. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering . pages 325-329. Cited by 1 (0.31year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction. In comparison with PCA, there is also superior performance in KPCA. applied kernel principal component analysis (KPCA) to SVM for feature extraction. The authors examined sunspot data and one real futures contract, and found such feature extraction enhanced performance and also that KPCA was superior to PCA. YANG, Haiqin, 2003. Margin Variations in Support Vector Regression for the Stock Market Prediction. Degree of Master of Philosophy Thesis, Department of Computer Science Engineering, The Chinese University of Hong Kong, June 2003. Cited by 1 (0.31year) Abstract: Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the 949-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically, researchers have used methods such as crossvalidation or random selection to select a suitable 949 for that particular data set. In addition, financial time series are usually embedded with noise and the associated risk varies with time. Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly. In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin. By varying the width of the margin, we can reflect the change of volatility in the financial data by controlling the symmetry of margins, we are able to reduce the downside risk. Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property. For setting the width of margin, the Momentum (also including asymmetrical margin control) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are considered. Experiments are performed on two indices: Hang Seng Index (HSI) and Dow Jones Industrial Average (DJIA) for the Momentum method and three indices: Nikkei225, DJIA and FTSE100, for GARCH models, respectively. The experimental results indicate that these methods improve the predictive performance comparing with the standard SVR and benchmark model. On the study of the symmetry property, we give a sufficient condition to prove that the predicted value is monotone decreasing to the increase of the up margin. Therefore, we can reduce the predictive downside risk, or keep it zero, by increasing the up margin. An algorithm is also proposed to test the validity of this condition, such that we may know the changing trend of predictive downside risk by only running this algorithm on the training data set without performing actual prediction procedure. Experimental results also validate our analysis. employs SVMs for regression and varys the width of the margin to reflect the change of volatility and controls the symmetry of margins to reduce the downside risk. Results were positive. CALVO, Rafael A. and Ken WILLIAMS, 2002. Automatic Categorization of Announcements on the Australian Stock Exchange. Cited by 1 (0.24year) Abstract: This paper compares the performance of several machine learning algorithms for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The article also describes some of the applications that the categorization of corporate announcements may enable. We have performed tests on two categorization tasks: market sensitivity, which indicates whether an announcement will have an impact on the market, and report type, which classifies each announcement into one of the report categories defined by the ASX. We have tried Neural Networks, a Na239ve Bayes classifier, and Support Vector Machines and achieved good resultspared the performance of neural networks, a na ve bayes classifier, and SVMs for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The results were all good, but with the SVM underperforming the other two models. AHMED, A. H.M. T. 2000. Forecasting of foreign exchange rate time series using support vector regression. 3rd year project. Computer Science Department, University of Manchester. Cited by 1 (0.16year)used support vector regression for forecasting a foreign exchange rate time series. GUESDE, Bazile, 2000. Predicting foreign exchange rates with support vector regression machines. MSc thesis. Computer Science Department, University of Manchester. Cited by 1 (0.16year) Abstract: This thesis investigates how Support Vector Regression can be applied to forecasting foreign exchange rates. At first we introduce the reader to this non linear kernel based regression and demonstrate how it can be used for time series prediction. Then we define a predictive framework and apply it to the Canadian exchange rates. But the non-stationarity in the data, which we here define as a drift in the map of the dynamics, forces us to present and use the typical learning processes for catching different dynamics. Our implementation of these solutions include Clusters of Volatility and competing experts. Finally those experts are used in a financial vote trading system and substantial profits are achieved. Through out the thesis we hope the reader will be intrigued by the results of our analysis and be encouraged in other dircetions for further research. used SVMs for regression to predict the Canadian exchange rate, wisely recognised the problem of nonstationarity, dealt with it using experts and claimed that substantial profits were achieved. BAO, Yu-Kun, et al. . 2005. Forecasting Stock Composite Index by Fuzzy Support Vector Machines Regression. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Volume 6 . pages 3535-3540. not cited (0year) Abstract: Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting. used fuzzy support vector machines regression (FSVMR) to forecast a data set from the Shanghai Stock Exchange with positive results. CHEN, Kuan-Yu and Chia-Hui HO, 2005. An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting. ICNNB 05: International Conference on Neural Networks and Brain, 2005, Volume 3 not cited (0year) Abstract: This study applies a novel neural network technique, Support Vector Regression (SVR), to Taiwan Stock Exchange Market Weighted Index (TAIEX) forecasting. To build an effective SVR model, SVRs parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVRs optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the Normalized Mean Square Error (NMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node. used an SVM for regression for forecasting the Taiwan Stock Exchange Market Weighted Index (TAIEX). The results demonstrated that the SVR outperformed the ANN and RW models. CHEN, Wun-Hwa and Jen-Ying SHIH, 2006. A study of Taiwan39s issuer credit rating systems using support vector machines. Expert Systems with Applications . Volume 30, Issue 3, April 2006, Pages 427-435. not cited (0year) By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62) is also higher than previous research. used an SVM to classify Taiwans issuer credit ratings and found that it performed better than the back propagation neural network (BP) model. CHEN, Wun-Hua, Jen-Ying SHIH and Soushan WU, 2006. Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. International Journal of Electronic Finance . Volume, Issue 1, pages 49-67. not cited (0year) Abstract: Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researchespared SVMs and back propagation (BP) neural networks when forecasting the six major Asian stock markets. Both models perform better than the benchmark AR (1) model in the deviation measurement criteria, whilst SVMs performed better than the BP model in four out of six markets. GAVRISHCHAKA, Valeriy V. and Supriya BANERJEE, 2006. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting. Computational Management Science . Volume 3, Number 2 (April 2006), Pages 147-160. not cited (0year) Abstract: Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data. used SVMs for forecasting stock market volatility with positive results. HOVSEPIAN, K. and P. ANSELMO, 2005. Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines. ICNNampB3905: International Conference on Neural Networks and Brain, 2005, Volume 3 . Pages 1656-1660. not cited (0year) Abstract: We outline technological issues and our fimdings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC trainingtesting set. We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVCs decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach. used SVMs for classification to predict relative volatility clusters and achieved accurate and robust results. INCE, H. and T. B. TRAFALIS, 2004. Kernel principal component analysis and support vector machines for stock price prediction. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Volume 3 . pages 2053-2058. not cited (0year) Abstract: Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique. found that MLP neural networks outperform support vector regression when applied to stock price prediction. KAMRUZZAMAN, Joarder, Ruhul A SARKER and Iftekhar AHMAD, 2003. SVM Based Models for Predicting Foreign Currency Exchange Rates. Proceedings of the Third IEEE International Conference on Data Mining (ICDM03) . Pages 557-560. not cited (0year) Abstract: Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e. g. neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and varepsilon - insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented. investigated the effect of different kernel functions and the regularization parameter when using SVMs to predict six different foreign currency exchange rates against the Australian dollar. investigated comprehensible credit scoring models using rule extraction from SVMs. NALBANTOV, Georgi, Rob BAUER and Ida SPRINKHUIZEN-KUYPER, 2006. Equity Style Timing Using Support Vector Regressions. to appear in Applied Financial Economics . not cited (0year) Abstract: The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study we examine whether the short-term variation in the U. S. size and value premium is predictable. We document style-timing strategies based on technical and (macro-)economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. Our findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons. used SVMs for regression for equity style timing with positive results. ONGSRITRAKUL, P. and N. SOONTHORNPHISAJ, 2003. Apply decision tree and support vector regression to predict the gold price. Proceedings of the International Joint Conference on Neural Networks, 2003, Volume 4 . Pages 2488-2492. not cited (0year) Abstract: Recently, support vector regression (SVR) was proposed to resolve time series prediction and regression problems. In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price. We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes. Our experimental results show that the combination of the decision tree and SVR leads to a better performance. applied a decision tree algorithm for feature selection and then performed support vector regression to predict the gold price, their results were positive. Van GESTEL, Tony, et al. . 2005. Linear and non-linear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk . Vol. 1, No. 4, Fall 2005, Pages 31-60. not cited (0year) Abstract: The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions and finally SVMs are added to capture remaining multivariate non-linear relations. apply linear and non-linear credit scoring by combining logistic regression and SVMs. YANG, Haiqin, et al. . 2004. Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. Neural Information Processing: 11th International Conference, ICONIP 2004, Calcutta, India, November 2004, Proceedings not cited (0year) Abstract: Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel 8220two-phase8221 SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed 8220two-phase8221 algorithm has improvement on the prediction. proposed a novel two-phase SVR training procedure to detect and deflate the influence of outliers. The method was tested on the Hang Seng Index, NASDAQ and FSTE 100 index and results were positive. However, its not clear why the significance of outliers (such as market crashes) should be understated. YU, Lean, Shouyang WANG and Kin Keung LAI, 2005. Mining Stock Market Tendency Using GA-Based Support Vector Machines. Internet and Network Economics: First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005, Proceedings (Lecture Notes in Computer Science) edited by Xiaotie Deng and Yinyu Ye, pages 336-345. not cited (0year) Abstract: In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e. g. statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. applied a random walk (RW) model, an autoregressive integrated moving average (ARIMA) model, an individual back-propagation neural network (BPNN) model, an individual SVM model and a genetic algorithm-based SVM (GASVM) to the task of predicting the direction of change in the daily SP500 stock price index and found that their proposed GASVM model performed the best. HARLAND, Zac, 2002. Using Support Vector Machines to Trade Aluminium on the LME.. Proceedings of the Ninth International Conference, Forecasting Financial Markets: Advances For Exchange Rates, Interest Rates and Asset Management . edited by C. Dunis and M. Dempster. not listed Abstract: This paper describes and evaluates the use of support vector regression to trade the three month Aluminium futures contract on the London Metal Exchange, over the period June 1987 to November 1999. The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data. The algorithm is founded on ideas derived from statistical learning theory and can be understood intuitively within a geometric framework. In this paper we use support vector regression to develop a number of trading submodels that when combined, result in a final model that exhibits above-average returns on out of sample data, thus providing some evidence that the aluminium futures price is less than efficient. Whether these inefficiencies will continue into the future is unknown. used an ensemble of SVMs for regression to trade the three month Aluminium futures contract on the London Metal Exchange with positive results. Van GESTEL, T. et al. . 2005. Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines, Workshop on Machine Learning in Finance, NIPS 2005 Conference, Whistler (British Columbia, Canada), Dec. 9.not listeddeveloped credit rating systems by combining linear ordinal logistic regression and fixed-size least squares SVMs. Forex predictions It is very difficult to predict how the market price of a currency will move in relation to another currency. Currency exchange rates are impacted by such as wide host of factors, including psychological ones and the intrinsic herd-mentality of speculative markets. Sometimes a simple rumor is enough to make a currency sink like a stone, at least temporary. Not only is it difficult to predict how the forex market will react to something, but it is also notoriously difficult to predict how strong that reaction will be, and what any counter-reactions will look like. This can make it hard to trade successfully with leveraged Forex certificates. A temporary reaction in the market can wipe out your position even if you are correct about the long term trend. It can often be better to use financial instrument such as binary options to benefit from trends on the currency market. Binary options give a good return and will not be affected by temporary drops in the market. The only thing that matters is the currency price at the time of maturity. Most Forex brokers do not offer trade with binary options. You will need a binary options broker account if you want to trade with binary options. Examples of factors that can influence the price of a currency in relation to other currencies The overall economic situation of the issuer of the currency. A strong economy will often mean a strong currency as well. Of course, if the currency becomes very highly valued, this can become problematic for export companies, and a problematic economic situation can arise for certain sectors of the country. At the same time, other sectors can be doing great since they profit from the low-cost of imported goods. The commercial balance of the issuer of the currency. A trade-deficiency will normally lead to a weakening of the currency. The political situation for the issuer of the currency. Unrest and instability will typically cause a drop in currency value. A stable political situation that is still not a good political situation can translate into a currency that is low value, but stable. Targeted speculation by one or several major currency traders. Sometimes even a comparatively small purchase or sale can be sufficient to trigger other traders to act in certain ways. It is often difficult to pin-point one specific reason for a currency to be weak or strong, or go up or down, since factors such as these tend to be intertwined with each other. The economic situation Different traders can also have different ideas about what actually constitutes good political and economic situation. There is for instance those who are very focused on Gross Domestic Product (GDP), while others prefer to also look at GDP at purchasing power parity per capita. Other important factors are national debt, retail sales and employmentunemploymentunderemployment statistics. One things that is very likely to cause a dramatic drop in currency value is an issuer that struggles to pay its debts. This will of course make the situation even worse for the issuer, if there are debts that must be paid in foreign currency. Commercial balance In the list above, the issuer8217s commercial balance is mentioned as one of the factors that can impact the market value of a currency. But what is this and how is it measured Commercial balance is the net export measured in local currency. If the issuer8217s (e. g. a country) exports are of a higher monetary value than the imports, the issuer has a positive commercial balance. If the value of the exports is smaller than the value of the imports, the commercial balance is negative. A negative commercial balance is also known as a trade deficit, and will typically bring the valuation of the currency down. Example: Country A exports a lot of high-value consumer goods. The countries that import all these products must pay for them using the currency of Country A. Therefore, the importing countries must purchase a lot of Country A currency at the forex market. The more sought after a currency is, the higher the price. The currency of Country A is therefore highly valued. If Country A had to import a lot of products, that could serve to bring the value of Country A currency down, since Country A would have to exchange a lot of its own currency for foreign currency at the forex market to pay for the imported goods. When Country A wants to sell a lot of its own currency, the availability of Country A currency at the forex market increases, and this impacts the demand-supply balance for Country A currency. It is important to remember that if the issuer is a country where producing goods for export is very important for the economy, the government might not want to see the currency get any stronger. A strong currency would make the exported products more expensive for foreign buyers, and the products might be out-competed by products produced in a country with a weaker currency. This would mean less revenue from exports, and probably also increased unemployment and underemployment as companies close down due to decreased foreign demand for their products. To avoid such a scenario, the government might take various actions in an effort to keep the currency from appreciating against other major currencies, and this is important for you to know if you are an FX trader. It should also be noted that a government might like the idea of having a low-valued currency since that can make domestically produced goods more sought after within the country, as imported goods becomes prohibitively expensive to purchase. Political stability and change When it comes to the forex market, political change can often have a larger impact than the overall political situation especially if we are looking at short-term fluctuations in exchange rates. This means that if something suddenly changes for the better for the issuer of a currency, the currency can appreciate markedly, even though the political situation is still very far from being good. The currency can appreciate in value against the currency of another country where the political situation is actually much better. The traders react to the change . Along the same lines, the valuation of a currency can drop sharply simply because a political situation is going from excellent to just fairly good. It should also be noted that sometimes a currency will appreciate simply as a reaction to the political situation in other countries. The political situation in Country A can be stable, but the currency is still going up like a rocket since the political situation in Country B, C and D is taking a turn for the worse and traders are rushing to own Country A currency. Of course, if Country A and Country B are neighbors or in any other way linked closely to each other, we might see the opposite thing happening. Country A is stable, but its currency is dropping in value anyway because traders fear that the political turmoil in Country B will soon impact Country A in a negative way. One of the reasons why fx traders shun political instability and social unrest is because they fear that investors (e. g. company owners) will pull out of the troublesome country or at the very least avoid making new investments. Such actions can lead to decreased demand for the currency, and traders don8217t want to find themselves stuck holding currency that few buyers want. Comentários estão fechados. Find what you are looking for Avoid Scams Learn more about FXSVM Based Models for Predicting Foreign Currency Exchange Rates Concepts in SVM Based Models for Predicting Foreign Currency Exchange Rates Bureau de change A bureau de change or currency exchange is a business whose customers exchange one currency for another. Although originally French, the term bureau de change is widely used throughout Europe, and European travellers can usually easily identify these facilities when in other European countries. It is also common to find a sign saying Exchange or Change. Since the adoption of the euro, many exchange offices incorporate its logotype prominently on their signage. more from Wikipedia Foreign exchange market The foreign exchange market (forex, FX, or currency market) is a form of exchange for the global decentralized trading of international currencies. Centros financeiros em todo o mundo funcionam como âncoras de negociação entre uma ampla gama de diferentes tipos de compradores e vendedores em torno do relógio, com exceção dos fins de semana. O mercado de câmbio determina os valores relativos de diferentes moedas. more from Wikipedia Support vector machine A support vector machine (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the input, making the SVM a non-probabilistic binary linear classifier. more from Wikipedia Exchange rate In finance, an exchange rate (also known as the foreign-exchange rate, forex rate or FX rate) between two currencies is the rate at which one currency will be exchanged for another. It is also regarded as the value of one countrys currency in terms of another currency. For example, an interbank exchange rate of 91 Japanese yen (JPY, ) to the United States dollar (US) means that 91 will be exchanged for each US1 or that US1 will be exchanged for each 91. more from Wikipedia Positive-definite kernel In operator theory, a branch of mathematics, a positive definite kernel is a generalization of a positive-definite matrix. more from Wikipedia Forecasting Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. more from Wikipedia Regularization (mathematics) In mathematics and statistics, particularly in the fields of machine learning and inverse problems, regularization involves introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information is usually of the form of a penalty for complexity, such as restrictions for smoothness or bounds on the vector space norm. A theoretical justification for regularization is that it attempts to impose Occams razor on the solution. more from Wikipedia Tools and Resources Publisher Site Contact Us Switch to single page view (no tabs) Javascript is not enabled and is required for the tabbed view or switch to the single page view The ACM Digital Library is published by the Association for Computing Machinery. Copyright copy 2017 ACM, Inc. 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