Comparison of ARIMA and LSTM for Stock Price Prediction

Abstract

The prediction of time series is an extremely important and challenging part, and due to some other unavoidable external factors, it can also affect its prediction results. Therefore, in order to compare which model can produce better predictions, this paper explores the following two models. First of all, the more traditional and classical model is the "Autoregressive Integrated Moving Average" (ARIMA). Moreover, according to the continuous exploration and development over the years, more variables have evolved, such as SRIMA (seasonal ARIMA), etc. This model shows considerable advantages in short-term prediction but has more disadvantages in the long term, which is not the best choice. Another model is closer to today's emerging technologies and more dependent on artificial intelligence data analysis, such as "Convolutional Neural Network" (CNN) and "Recurrent Neural Network" (RNN), among which there are many variants. This paper focuses on a special variant, Long Short-Term Memory (LSTM) neural network, which can learn from past data and relate to current data. Furthermore, it is found from the data in this paper that LSTM's prediction of data is better than ARIMA model.