Learn how to apply the principles of machine learning to time
series modeling with this indispensable resource
Machine Learning for Time Series Forecasting with Python is an
incisive and straightforward examination of one of the most crucial
elements of decision-making in finance, marketing, education, and
healthcare: time series modeling.
Despite the centrality of time series forecasting, few business analysts
are familiar with the power or utility of applying machine learning to
time series modeling. Author Francesca Lazzeri, a distinguished machine
learning scientist and economist, corrects that deficiency by providing
readers with comprehensive and approachable explanation and treatment of
the application of machine learning to time series forecasting.
Written for readers who have little to no experience in time series
forecasting or machine learning, the book comprehensively covers all the
topics necessary to:
- Understand time series forecasting concepts, such as stationarity,
horizon, trend, and seasonality
- Prepare time series data for modeling
- Evaluate time series forecasting models' performance and accuracy
- Understand when to use neural networks instead of traditional time
series models in time series forecasting
Machine Learning for Time Series Forecasting with Python is full
real-world examples, resources and concrete strategies to help readers
explore and transform data and develop usable, practical time series
forecasts.
Perfect for entry-level data scientists, business analysts, developers,
and researchers, this book is an invaluable and indispensable guide to
the fundamental and advanced concepts of machine learning applied to
time series modeling.
