A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine

被引：0

作者：

Ritzmann Junior, Norberto ^{[1
]}

Nievola, Julio Cesar ^{[1
]}

机构：

[1] Pontificia Univ Catolica Parana, Programa Posgrad Informat, Curitiba, Parana, Brazil

来源：

2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2018年

关键词：

genetic algorithm; support vector machine; generalized model prediction; time series; stock market; FEATURE-SELECTION; NEURAL-NETWORKS; STOCK; PREDICTION; MOVEMENT;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose the genetic algorithm for time window optimization, which is an embedded genetic algorithm (GA), to optimize the time window (TW) of the attributes using feature selection and support vector machine. This GA is evolved using the results of a trading simulation, and it determines the best TW for each technical indicator. An appropriate evaluation was conducted using a walk-forward trading simulation, and the trained model was verified to be generalizable for forecasting other stock data. The results show that using the GA to determine the TW can improve the rate of return, leading to better prediction models than those resulting from using the default TW.

引用

页数：8

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