In this chapter, you will be introduced to the basic concepts of Time Series Data and Regression. First, we distinguish some of the basic concepts such as Trend, Seasonality, and Noise, along with the principles of Lineal Regression using the Python library scikit-learn. Then, we will introduce the Historic Gold Prices time series and see how to perform a nonlinear forecast using Kernel Ridge Regression. Later, we will present a regression using the smoothed time series as the input.
This chapter will cover the following topics: