  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.
