March 2025
Intermediate to advanced
362 pages
9h 15m
Portuguese (Portugal, Brazil)
Content preview from Deep Learning para finanças
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Capítulo 9. Deep Learning para previsão de séries temporais II
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Este capítulo apresenta algumas técnicas e métodos para complementar a tarefa de previsão dos algoritmos de aprendizagem automática e profunda. É composto por diferentes tópicos, cada um dos quais aborda uma forma de melhorar e otimizar o processo. Neste ponto, deves ter uma boa compreensão dos conceitos básicos dos modelos de aprendizagem automática e profunda e sabes como codificar um algoritmo básico que prevê os retornos de uma série temporal financeira (ou qualquer série temporal estacionária). Este capítulo preenche a lacuna entre o conhecimento básico e o conhecimento avançado necessário para elevar os algoritmos a um nível funcional.
Diferenciação fraccionária
No seu livro Advances in Financial Machine Learning, Marcos López de Prado descreve uma técnica para transformar dados não estacionários em dados estacionários. É a chamada diferenciação fraccional.
A diferenciação fraccionada é uma técnica matemática utilizada para transformar uma série temporal numa série estacionária, preservando alguma da sua memória. Alarga o conceito de diferenciação (ou obtenção dos retornos), que é normalmente utilizado para remover tendências e tornar as séries cronológicas estacionárias.
Na diferenciação tradicional, a sequência de dados é diferenciada por um número inteiro, normalmente 1, o que implica subtrair ...
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