Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods

TL;DR

This paper compares traditional time series models and machine learning methods for financial series prediction, demonstrating that machine learning significantly outperforms traditional models in prediction accuracy using real stock data.

Contribution

It provides an empirical comparison showing the superior prediction precision of machine learning models over traditional time series approaches in financial forecasting.

Findings

Machine learning models outperform traditional models in prediction accuracy.

Deep learning models achieve the highest precision among tested methods.

Traditional models like ARIMA and GARCH are less effective in noisy financial data.

Abstract

Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning, as an emerging research field in recent years, has brought about many incredible improvements in tasks such as regressing and classifying, and it's also promising to exploit the methodology in financial time series predicting. In this paper, the predicting precision of financial time series between traditional time series models and mainstream machine learning models including some state-of-the-art ones of deep learning are compared through experiment using real stock index data from history. The result shows that machine learning as a modern method far surpasses traditional models in precision.