This course aims to compensate a missing dimensionin Data Science/Machine Learning studies addressing the analysis of data which changes over time, that is time-series. It will provide students with the toolsfor analyzing time-series data. The course start by building a background on random/stochastic processes and frequency transforms. We will then discuss parametric processmodels for time-series such as AR, ARMA, etc and provide classical estimationmethods. We will then extend the discussion to prediction and forecasting. Unlike classical courses on Time-Series Analysis we will cover alsonon-stationary time series, introducing methods and transforms. We will alsoextend multivariate analysis to graph time series. We aim to present also implementations using R or Python.
