Overview
In the last 15 years, business requests related to time series forecasting changed dramatically. Business needs evolved from predicting at most 100, low frequency data, to forecasting 10.000, high frequency time series. Unfortunately, the classical tools may not be the best anymore, both in terms of accuracy and computationally. Hence, nowadays the “time series forecasting” data scientist is required to be capable of providing business forecasting solutions tackling both scalability and accuracy, constantly keeping up-to-date with new methods.
The aim of the course is to teach how time series forecasting problems can be solved in practice. The state-of-the-art techniques are presented from a very practical point of view, throughout R tutorials. Python algorithms are also presented and used within R by means of the reticulate package.