This textbook presents methods and techniques for time series analysis and forecasting and
shows how to use Python to implement them and solve data science problems. It covers not only
common statistical approaches and time series models including ARMA SARIMA VAR GARCH and
state space and Markov switching models for (non)stationary multivariate and financial time
series but also modern machine learning procedures and challenges for time series forecasting.
Providing an organic combination of the principles of time series analysis and Python
programming it enables the reader to study methods and techniques and practice writing and
running Python code at the same time. Its data-driven approach to analyzing and modeling time
series data helps new learners to visualize and interpret both the raw data and its computed
results. Primarily intended for students of statistics economics and data science with an
undergraduate knowledge of probability and statistics the book will equally appeal to industry
professionals in the fields of artificial intelligence and data science and anyone interested
in using Python to solve time series problems.
