Stacked BI-LSTM and E-Optimized CNN-A Hybrid Deep Learning Model for Stock Price Prediction

IF 1 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI:10.3103/s1060992x24700024

Swarnalata Rath, Nilima R. Das, Binod Kumar Pattanayak

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Abstract

Univariate stocks and multivariate equities are more common due to partnerships. Accurate future stock predictions benefit investors and stakeholders. The study has limitations, but hybrid architectures can outperform single deep learning approach (DL) in price prediction. This study presents a hybrid attention-based optimal DL model that leverages multiple neural networks to enhance stock price prediction accuracy. The model uses strategic optimization of individual model components, extracting crucial insights from stock price time series data. The process involves initial pre-processing, wavelet transform denoising, and min-max normalization, followed by data division into training and test sets. The proposed model integrates stacked Bi-directional Long Short Term Memory (Bi-LSTM), an attention module, and an Equilibrium optimized 1D Convolutional Neural Network (CNN). Stacked Bi-LSTM networks shoot enriched temporal features, while the attention mechanism reduces historical data loss and highlights significant information. A dropout layer with tailored dropout rates is introduced to address overfitting. The Conv1D layer within the 1D CNN detects abrupt data changes using residual features from the dropout layer. The model incorporates Equilibrium Optimization (EO) for training the CNN, allowing the algorithm to select optimal weights based on mean square error. Model efficiency is evaluated through diverse metrics, including Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2), to confirm the model’s predictive performance.

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叠加 BI-LSTM 和 E-Optimized CNN--用于股价预测的混合深度学习模型

摘要由于伙伴关系，多元股票和多变量股票更为常见。准确的未来股票预测有利于投资者和利益相关者。研究存在局限性，但混合架构在价格预测方面的表现可以优于单一深度学习方法（DL）。本研究提出了一种基于注意力的混合优化 DL 模型，该模型利用多个神经网络来提高股票价格预测的准确性。该模型对各个模型组件进行了战略性优化，从股票价格时间序列数据中提取了重要的见解。这一过程包括初始预处理、小波变换去噪和最小-最大归一化，然后将数据分为训练集和测试集。建议的模型集成了堆叠双向长短期记忆（Bi-LSTM）、注意力模块和均衡优化的一维卷积神经网络（CNN）。堆叠的双向长时短时记忆（Bi-LSTM）网络可拍摄丰富的时间特征，而注意力机制可减少历史数据丢失并突出重要信息。为解决过拟合问题，还引入了具有量身定制的丢失率的丢失层。1D CNN 中的 Conv1D 层利用剔除层的残余特征检测数据的突然变化。该模型在训练 CNN 时采用了均衡优化（EO）技术，允许算法根据均方误差选择最佳权重。模型效率通过各种指标进行评估，包括均方误差 (MAE)、均方误差 (MSE)、均方根误差 (RMSE) 和 R 平方 (R2)，以确认模型的预测性能。

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.