Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

TL;DR

This paper compares traditional ARIMA models with modern LSTM deep learning algorithms for forecasting economic and financial time series, finding LSTM significantly reduces prediction errors and is superior to ARIMA.

Contribution

The study provides empirical evidence that LSTM outperforms ARIMA in forecasting accuracy for financial time series, highlighting the effectiveness of deep learning methods.

Findings

LSTM reduces error rates by 84-87% compared to ARIMA.

Training epochs do not significantly affect LSTM performance.

LSTM demonstrates superior forecasting accuracy over traditional models.

Abstract

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as "epoch" in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.