Open Access
Research Article
Stock market prediction using ARIMA-LSTM hybrid
*Aayushi PandyaCorresponding authoraayushipandya333@gmail.comDepartment of Information Technology Institute of Engineering & Technology Devi Ahilya VishwavidyalayaIndore, Madhya Pradesh, IndiaView full profile → , Vivek Kapoorvkapoor@ietdavv.edu.inDepartment of Information Technology Institute of Engineering & Technology Devi Ahilya VishwavidyalayaDepartment of Information Technology Institute of Engineering and Technology Devi Ahilya UniversityIndore, Madhya Pradesh, 452017, IndiaView full profile → , Apash Joshijoshiapash017@gmail.comBusiness Information Systems University of Applied Sciences and Arts Northwestern Switzerland FHNWWindisch, SwitzerlandView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 29 May 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1697
- Pages:
- 1129–1139
Abstract
The nation’s economy greatly depends on the stock market. A healthy stock market can boost the economy, but if not going well, it can also trigger a recession. It is one of the most volatile markets, it is subjected to sudden changes due to variety of factors including interest rates, supply and demand, inflation, political issues, natural disasters, etc. Therefore, accurate stock market forecasting is crucial in order to help stock customers. Many models which have already been worked on for predicting stock prices are using techniques like ARIMA and LSTM. But hybrid models are now being developed for better outcomes. This paper proposes composition of ARIMA and LSTM for stock prediction and compares the results of ARIMA, LSTM and ARIMA-LSTM hybrid. To test the effectiveness of the hybrid model to others, the model is applied to 5 businesses from various industries. For comparison, different errors are taken into account.
Keywords
Subject Classifications
Primary 93A30Secondary 49K15
References
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