Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods
This addresses the problem of improving prediction accuracy for financial analysts and investors, but it is incremental as it compares existing methods without introducing new ones.
The paper compared the precision of traditional time series models (e.g., ARIMA, GARCH) with machine learning methods for financial series prediction using real stock index data, finding that machine learning far surpasses traditional models in precision.
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.