Comparative Analysis of LSTM and ARIMA Models in Stock Price Prediction: A Technology Company Example

Abstract

Stock price forecasting has been an important area of interest for economists and computer scientists. In addition to traditional statistical methods, advanced artificial intelligence techniques such as machine learning can stand out with their ability to process complex data sets and adapt to historical data. In recent years, hybrid models combining deep learning and time series methods have demonstrated superior performance in stock selection and portfolio optimization. This study comparatively analyses the performance of LSTM and ARIMA models in time series forecasting. In the study, the stock prices of Oracle company are predicted using two different models, LSTM and ARIMA. Model performance is evaluated using metrics like MSE, MAE, RMSE, and MAPE. Both models have been found to be successful in different metrics. The LSTM model has lower error values; meanwhile, the ARIMA model produced proportionally more accurate forecasts. The study concludes that given the potential offered by deep learning, models such as LSTM are essential for time series forecasting. The flexibility of deep learning allows the development of customized models for different data types and time series problems.

Keywords

• LSTM
• ARIMA
• Time series
• Stock price forecasting

References

1. Agrawal JG, Chourasia V, Mittra A. 2013. State-of-the-art in stock prediction techniques. Int J Adv Res Electr Electron Instrum Eng, 2(4): 1360-1366.
2. Bustos O, Quimbaya A. 2020. Stock market movement forecast: A systematic review. Expert Syst Appl, 156: 113464.
3. Choi J, Yoo S, Zhou X, Kim Y. 2023. Hybrid information mixing module for stock movement prediction. IEEE Access, 11: 28781-28790.
4. Choy YT, Hoo MH, Khor KC. 2021. Price prediction using time-series algorithms for stocks listed on Bursa Malaysia. In: 2nd International Conference on Artificial Intelligence and Data Sciences, 8-9 September, Piscataway, New Jersey, USA, pp: 1-5.
5. Gandhmal DP, Kumar K. 2019. Systematic analysis and review of stock market prediction techniques. Comput Sci Rev, 34: 100190.
6. Jadhav R, Sinha S, Wattamwar S, Kosamkar P. 2021. Leveraging Market Sentiment for Stock Price Prediction using GAN. In: 2nd Global Conference for Advancement in Technology, 01-03 October, 2021, Bangalore, India, pp: 1-6.
7. Kanavos A, Vonitsanos G, Mohasseb A, Mylonas P. 2020. An entropy-based evaluation for sentiment analysis of stock market prices using Twitter data. In: 15th International Workshop on Semantic and Social Media Adaptation and Personalization, 29-30 October Zakynthos, Greece, pp: 1-7.
8. Khan S, Alghulaiakh H. 2020. ARIMA model for accurate time series stocks forecasting. Int J Adv Comput Sci Appl, 11(7).

9. Kontopoulou V, Panagopoulos AD, Kakkos I, Matsopoulos GK. 2023. A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks. Future Int, 15(8): 255.
10. Kumar SV, Vanajakshi L. 2015. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur Transp Res Rev, 7: 1-9.
11. Kumbure MM, Lohrmann C, Luukka P, Porras J. 2022. Machine learning techniques and data for stock market forecasting: A literature review. Expert Syst Appl, 197: 116659.
12. Lipton ZC, Kale DC, Elkan C, Wetzel R. 2015. Learning to diagnose with LSTM recurrent neural networks. ArXiv, 2015: 1511.
13. Lu W, Ge W, Li R, Yang L. 2021. A Comparative Study on the Individual Stock Price Prediction with the Application of Neural Network Models. In: International Conference on Computer Engineering and Artificial Intelligence, 27-29 August, Shanghai, China, pp: 235-238.
14. Nabipour M, Nayyeri P, Jabani H, Mosavi A, Salwana E. 2020. Deep learning for stock market prediction. Entropy, 22(8): 840.
15. Nti IK, Adekoya AF, Weyori BA. 2020. A systematic review of fundamental and technical analysis of stock market predictions. Artif Intell Rev, 53(4): 3007-3057.
16. Ojo SO, Owolawi PA, Mphahlele M, Adisa JA. 2019. Stock market behaviour prediction using stacked LSTM networks. In: International Multidisciplinary Information Technology and Engineering Conference, 21-22 November, Vanderbijlpark, South Africa, pp: 1-5.
17. Oracle Corporation Stock Historical Prices & Data Yahoo Finance. URL: https://finance.yahoo.com/quote/ORCL/history (accessed date: January 29, 2024).
18. Rouf N, Malik MB, Arif T, Sharma S, Singh S, Aich S, Kim HC. 2021. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21): 2717.
19. Ruan J, Wu W, Luo J. 2020. Stock Price Prediction Under Anomalous Circumstances. In: IEEE International Conference on Big Data, pp: 4787-4794.
20. Sarvesh S, Sidharth RV, Vaishnav V, Thangakumar J, Sathyalakshmi S. 2021. A hybrid model for stock price prediction using machine learning techniques with CNN. In: 5th International Conference on Information Systems and Computer Networks, pp: 1-6.
21. Shah D, Isah H, Zulkernine F. 2019. Stock market analysis: A review and taxonomy of prediction techniques. Int J Financ Stud, 7(2): 26.
22. Sheng Y, Fu K, Wang L. 2022. A PCA-LSTM Model for Stock Index Forecasting: A Case Study in Shanghai Composite Index. In: 7th International Conference on Cloud Computing and Big Data Analytics, pp: 412-417.
23. Sherstinsky A. 2020. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D, 404: 132306.
24. Singh A, Bansal A, Nair A, Kaushal A. 2022. Predictive Analytics of Stock Market as a Time Series. In: 4th International Conference on Advances in Computing, Communication Control and Networking, pp: 770-777.
25. Singh P, Jha M, Sharaf M, El-Meligy MA, Gadekallu TR. 2023. Harnessing a Hybrid CNN-LSTM Model for Portfolio Performance: A Case Study on Stock Selection and Optimization. IEEE Access, 11: 104000-104015.
26. Sisodia PS, Gupta A, Kumar Y, Ameta GK. 2022. Stock market analysis and prediction for NIFTY50 using LSTM Deep Learning Approach. In: 2nd international conference on innovative practices in technology and management, pp: 156-161.
27. Staudemeyer RC, Morris ER. 2019. Understanding LSTM a tutorial into long short-term memory recurrent neural networks, ArXiv, 2019: 1909.
28. Strader TJ, Rozycki JJ, Root TH, Huang YHJ. 2020. Machine learning stock market prediction studies: review and research directions. J Int Technol Inf Manage, 28(4): 63-83.
29. Van Houdt G, Mosquera C, Nápoles G. 2020. A review on the long short-term memory model. Artif Intell Rev, 53(8): 5929-5955.
30. Yang S, Yu X, Zhou Y. 2020. Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. In: International workshop on electronic communication and artificial intelligence, pp: 98-101.