Comparison between autoregressive integrated moving average and long short term memory models for stock price prediction

Pi Rey Low, Eric Sakk


This study compares the forecasting accuracy in stock price prediction of two
widely established models - a more traditional autoregressive integrated
moving average (ARIMA) model and a deep learning network, the long shortterm memory (LSTM) model. They perform exceptionally well in time series data analysis and are applied to ten different stock tickers, comprising exchange-traded funds (ETFs) from different market sectors for the purpose of this study. The parameters in both models were optimised and this process revealed several differences from existing literature with regards to the optimal combination of parameters in both models. Upon comparing their performances, despite being more accurate when making point predictions, the ARIMA was outperformed significantly by LSTMs in terms of long-term predictions. Point predictions made by ARIMA were found to have similar accuracies as the long-run predictions made by LSTMs.


Artificial intelligence; Autoregressive integrated moving average; Deep learning; Long short-term memory; Parameter optimisation; Stock price forecasting; Time series data

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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