Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

Xinye Sha

doi:10.54254/2754-1169/91/20241031

Research Article

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Published on 20 June 2024

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Sha,X. (2024). Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization. Advances in Economics, Management and Political Sciences,91,142-149.

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Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

Xinye Sha *^,1,

¹ Columbia University, New York, 10027, USA

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/91/20241031

Abstract

In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.

Keywords

Time Series Stock, LSTM, Genetic Algorithm

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References

[1]. Shiri, Farhad Mortezapour, et al. "A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU." arxiv preprint arxiv:2305.17473 (2023).

[2]. Abou Houran, Mohamad, et al. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications." Applied Energy 349 (2023): 121638.

[3]. Zhao, Lingxiao, et al. "A hybrid VMD-LSTM/GRU model to predict non-stationary and irregular waves on the east coast of China." Ocean Engineering 276 (2023): 114136.

[4]. Limouni, Tariq, et al. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model." Renewable Energy 205 (2023): 1010-1024.

[5]. Redhu, Poonam, and Kranti Kumar. "Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM." Physica A: Statistical Mechanics and its Applications 625 (2023): 129001.

[6]. Wan, An**, et al. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism." Energy 282 (2023): 128274.

[7]. Osama, Omnia M., et al. "Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis." 2023 3rd International Conference on Electronic Engineering (ICEEM). IEEE, 2023.

[8]. Yin, Lirong, et al. "U-Net-LSTM: time series-enhanced lake boundary prediction model." Land 12.10 (2023): 1859.

[9]. Xu, Huanwei, et al. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries." Energy 276 (2023): 127585.

[10]. Cao, Yisheng, et al. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model." Energy 283 (2023): 128669.

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Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Management Research and Economic Development

Conference website: https://www.icmred.org/

ISBN：978-1-83558-479-8(Print) / 978-1-83558-480-4(Online)

Conference date: 30 May 2024

Editor：Canh Thien Dang

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.91

ISSN：2754-1169(Print) / 2754-1177(Online)

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