Summary
In this chapter, we explored the nature of time series, described their components, and implemented signal processing to smooth the time series. We examined a simple example of linear regression using scikit-learn. Then, we introduced the KRR implemented in the mlpy library. Finally, we presented two implementations of KRR, one with with the complete data and the other with the smoothed data, to predict the monthly gold price in June 2013. We found that, for this case, the prediction with the complete data was more accurate.
In the next chapter, you will learn how to perform a dimensionality reduction and how to implement a SVM with a multivariate dataset.
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