Summary
In this chapter, we explored the nature of time series, describing their components and implementing signal processing to smooth the time series. Then, we introduced the Kernel ridge regression (KRR) implemented in the mlpy library. Finally we presented two implementations of the KRR; one with the complete data and the other with the smoothed data, to predict the monthly gold price in June 2013 and we found that for this case the prediction with the complete data was more accurate.
In the next chapter, we will learn how to perform a dimensionality reduction and how to implement a support vector machine (SVM) with a multivariate dataset.
