Machine Learning for Time Series Forecasting with Python. Francesca Lazzeri

Through some practical examples of time series, we discussed some essential aspects of time series representations, modeling, and forecasting.

      Specifically, we discussed the following topics:

       Flavors of Machine Learning for Time Series Forecasting: In this section you learned a few standard definitions of important concepts, such as time series, time series analysis, and time series forecasting. You also discovered why time series forecasting is a fundamental cross-industry research area.

       Supervised Learning for Time Series Forecasting: In this section you learned how to reshape your forecasting scenario as a supervised learning problem and, as a consequence, get access to a large portfolio of linear and nonlinear machine learning algorithms.

       Python for Time Series Forecasting: In this section we looked at different Python libraries for time series data such as pandas, statsmodels, and scikit-learn.

       Experimental Setup for Time Series Forecasting: This section provided you with a general guide for setting up your Python environment for time series forecasting.

      In the next chapter, we will discuss some practical concepts such as the time series forecast framework and its applications. Moreover, you will learn about some of the caveats that data scientists working on forecasting projects may face. Finally, I will introduce a use case and some key techniques for building machine learning forecasting solutions successfully.