$1,010Python 科學計算和數據科學應用 : 使用 NumPy、SciPy 和 matplo, 2/e
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.
FRANCESCA LAZZERI is an accomplished economist who works with machine learning, artificial intelligence, and applied econometrics. She works at Microsoft as a data scientist and machine learning scientist to develop a portfolio of machine learning services. She is a sought-after speaker and has given popular talks at AI conferences and academic seminars at Berkeley, Harvard, and MIT.