Machine Learning for Time Series Forecasting with Python

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Machine Learning for Time Series Forecasting with Python is a comprehensive guide to applying machine learning to time series modeling, covering concepts, data preparation, model e valuation, and real-world examples. It is ideal for entry-level data scientists, business analysts, developers, and researchers. Format: Paperback / softback Length: 224 pages Publication date: 25 February 2021 Publisher: John Wiley & Sons Inc Machine Learning for Time Series Forecasting with Python is a comprehensive and accessible guide to one of the most critical aspects 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 explanations and treatments of the application of machine learning to time series forecasting. Written for readers with 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 of 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. Weight: 378g Dimension: 187 x 234 x 18 (mm) ISBN-13: 9781119682363 Show more