Overview of Time Series Forecasting
Francesca Lazzeri PhD · 2020
This chapter is dedicated to the conceptual introduction—with some practical examples—of time series, where the readers can learn the essential aspects of time series representations, modeling, and forecasting. It helps the readers to learn a few standard definitions of important concepts, such as time series, time series analysis, and time series forecasting, and discover why time series forecasting is a fundamental cross-industry research area. The chapter also helps the readers learn how to reshape their forecasting scenario as a supervised learning problem and, as a consequence, get access to a large portfolio of linear and nonlinear machine learning algorithms. It looks at different Python libraries for time series data and how libraries such as pandas, statsmodels, and scikit-learn can help the readers with data handling, time series modeling, and machine learning, respectively. The chapter provides the readers general advice for setting up their Python environment for time series forecasting.