Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction

计算机科学 期限(时间) 信息融合 股票价格 融合 短时记忆 算法 遗传算法 人工智能 数据挖掘 机器学习 系列(地层学) 人工神经网络 生物 语言学 哲学 物理 量子力学 循环神经网络 古生物学
作者
Ankit Thakkar,Kinjal Chaudhari
出处
期刊:Applied Soft Computing [Elsevier]
卷期号:128: 109428-109428 被引量:28
标识
DOI:10.1016/j.asoc.2022.109428
摘要

Information fusion is one of the critical aspects in diverse fields of applications; while the collected data may provide certain perspectives, a fusion of such data can be a useful way of exploring, expanding, enhancing, and extracting meaningful information for a better organization of the targeted domain. A nature-inspired evolutionary approach, namely, genetic algorithm (GA) is adopted for a variety of applications including stock market prediction. The complex, highly fluctuating financial market-related problems require optimized models for reliable forecasting. Also, it can be observed that stock market etiquettes are generally non-linear in nature and therefore, a broader understanding and analysis of such market behaviors necessitate the collection and fusion of relevant information based on different associated factors. In this article, we propose an information fusion-based GA approach with inter-intra crossover and adaptive mutation (ICAN) for stock price and trend prediction. Inspired by the genetic diversity and survival capability of various organisms, our proposed approach aims to optimize parameters of a long short-term memory prediction model, and selects a set of features; to address these problems of interest, we integrate inter-chromosome as well as conditional intra-chromosome crossover operations along with adaptive mutation to diversify the potential chromosome solutions. We illustrate the step-by-step procedure followed by GA with ICAN and evaluate its performance for one-day-ahead stock price and trend prediction. GA with ICAN-based optimization results in an average reduction of 43%, 27%, and 26% using mean squared error, mean absolute error, and mean absolute percentage error, respectively, as compared to the existing GA-based optimization approaches; further, an average improvement of 61% is encountered using R 2 score. We also compare our work with Ant Lion Optimization approach and demonstrate the significance of GA with ICAN-based optimization. We analyze statistical significance, as well as convergence functions, for GA with ICAN and discuss remarkable performance enhancement; we provide necessary concluding remarks with potential future research directions. • Two parts in each chromosome are proposed to select features and optimize parameters. • Inter-chromosome crossover is individually applied to each part of chromosomes. • Information fusion-based conditional intra-chromosome crossover is performed. • Mutation rate is adaptively updated based on previous and current generation fitness. • Improved stock price trend prediction performance is demonstrated using GA with ICAN.
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