COMPARING THE PREDICTION ACCURACY OF LSTM AND ARIMA MODELS FOR TIME-SERIES WITH PERMANENT FLUCTUATION

Ghahreman Abdoli · Mohsen Mehrara · Mohammad Ebrahim Ardalani

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

In developing countries with an unstable economic system, permanent fluctuation in historical data is always a concern. Recognizing dependency and independency of variables are vague and proceeding a reliable forecast model is more complex than other countries. Although linearization of nonlinear multivariate economic time-series to predict, may give a result, the nature of data which shows irregularities in the economic system, should be ignored. New approaches of artificial neural network (ANN) help to make a prediction model with keeping data attributes. In this paper, we used the Tehran Stock Exchange (TSE) intraday data in 10 years to forecast the next 2 months. Long Short-Term Memory (LSTM) from ANN chooses and outputs compared with the autoregressive integrated moving average (ARIMA) model. The results show, although, in long term prediction, the forecast accuracy of both models reduce, LSTM outperforms ARIMA, in terms of error of accuracy, significantly.

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2003Time series forecasting using a hybrid ARIMA and neural network model 2017Deep learning with long short-term memory networks for financial market predictions 2014Stock Price Prediction Using the ARIMA Model 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2018Financial time series forecasting model based on CEEMDAN and LSTM 2018Deep learning-based feature engineering for stock price movement prediction 2018An innovative neural network approach for stock market prediction

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