<p><b>Learn how to apply the principles of machine learning to </b><b>time series modeling with this indispensable resource</b> </p> <p><i>Machine Learning for Time Series Forecasting with Python</i> 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. </p> <p>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. </p> <p>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: </p> <ul> <li>Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality </li> <li>Prepare time series data for modeling </li> <li>Evaluate time series forecasting models’ performance and accuracy </li> <li>Understand when to use neural networks instead of traditional time series models in time series forecasting </li> </ul> <p><i>Machine Learning for Time Series Forecasting with Python </i>is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. </p> <p>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. </p> <p> </p> <p> </p>
