| Financial time series prediction is a very important economical problem but the data available is very noisy. In this thesis, we explain the use of statistical and machine learning methods for stock market prediction and we evaluate the performance of these methods on data from the S&P/TSX 60 stock index. We use both linear regression and support vector regression, a state-of-art machine learning method, which is usually robust to noise. The results are mixed, illustrating the difficulty of the problem. We discuss the utility of using different types of data pre-processing for this task as well. |
