揭开深度学习的神秘面纱:LSTM、BiLSTM、GRU、BiGRU、RNN 比较

Q2 Mathematics
Z. M. Shaikh, S. Ramadass
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引用次数: 0

摘要

深度学习算法在时间序列分析方面取得了令人瞩目的成果,为各个领域带来了革命性的变化。在不同的架构中,递归神经网络(RNN)在序列数据处理中发挥了重要作用。本研究全面比较了著名的 RNN 变体:长短期记忆 (LSTM)、双向 LSTM (BiLSTM)、门控递归单元 (GRU)、双向 GRU (BiGRU) 和 RNN,以分析它们在印度国家证券交易所 (NSEI) 中各自的优缺点。为本研究开发的 Python 应用程序旨在评估和确定这些变体中最有效的算法。为进行评估,收集了从 2004 年 1 月 1 日至 2023 年 6 月 30 日期间公共领域的数据。数据集考虑了一些重大事件,如非货币化、市场崩溃、COVID-19 大流行、汽车行业衰退和失业率上升。分析选取了银行、汽车、石油天然气、金属和医药等多个行业的股票。最后,分析结果表明,不同股票的算法性能各不相同。具体来说,在某些情况下,BiLSTM 的表现优于其他算法,而在其他情况下,BiGRU 和 LSTM 的表现都超过了其他算法。值得注意的是,在所有股票中,简单 RNN 的整体性能始终最低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison
Deep learning algorithms have revolutionized various fields by achieving remarkable results in time series analysis. Among the different architectures, recurrent neural networks (RNNs) have played a significant role in sequential data processing. This study presents a comprehensive comparison of prominent RNN variants: long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional GRU (BiGRU), and RNN, to analyze their respective strengths and weaknesses of national stock exchange India (NSEI). The Python application developed for this research aims to evaluate and determine the most effective algorithm among the variants. To conduct the evaluation, data from the public domain covering the period from 1/1/2004 to 30/06/2023 is collected. The dataset considers significant events such as demonetization, market crashes, the COVID-19 pandemic, downturns in the automobile sector, and rises in unemployment. Stocks from various sectors including banking, automobile, oil and gas, metal, and Pharma are selected for analysis. Finally, the results reveal that algorithm performance varies across different stocks. Specifically, in certain cases, BiLSTM outperforms, while in others, both BiGRU and LSTM are surpassed. Notably, the overall performance of simple RNN is consistently the lowest across all stocks.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
782
期刊介绍: The aim of Indonesian Journal of Electrical Engineering and Computer Science (formerly TELKOMNIKA Indonesian Journal of Electrical Engineering) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the applications of Telecommunication and Information Technology, Applied Computing and Computer, Instrumentation and Control, Electrical (Power), Electronics Engineering and Informatics which covers, but not limited to, the following scope: Signal Processing[...] Electronics[...] Electrical[...] Telecommunication[...] Instrumentation & Control[...] Computing and Informatics[...]
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