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Comparison of ARIMA and LSTM in Different Industries
- 1 Shenzhen University
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Abstract
In the realm of finance, stock price prediction holds significant importance, and the breakthroughs in deep learning have provided new solutions to this problem. This study compares deep learning methods with traditional time series models by employing a time series model, namely Arima, and the neural network LSTM model to predict the closing stock data of Pfizer Inc. (PFE) in the healthcare sector, Alibaba Group Holding Limited in the e-commerce sector, EA Sports in the gaming industry, and NVIDIA Corporation in the AI industry over the past year. The aim is to identify the most suitable prediction method for each company in stock price forecasting. Through the comparison, it is observed that, regardless of the company, ARIMA outperforms LSTM in stock price prediction. Therefore, it can be concluded that, among these two models, ARIMA is a better fit for stock price prediction in these four companies. Furthermore, it is inferred that this method may also be applicable to other companies within the respective industries. However, further research and data collection are necessary to validate and support this inference. Overall, this study demonstrates the superiority of the ARIMA model over LSTM in stock price prediction for the selected companies in the healthcare, e-commerce, gaming, and AI industries.
Keywords
Arima, LSTM, MSE, stock price predictions
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Cite this article
Li,H. (2024). Comparison of ARIMA and LSTM in Different Industries. Advances in Economics, Management and Political Sciences,57,294-302.
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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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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