May 1, 2024
3 minute read
Time-series forecasting is a technique used to predict future values of a time-series dataset based on its historical values. It involves the analysis of data points collected over time to identify patterns, trends, and seasonality. By understanding these patterns, businesses and individuals can make informed decisions and prepare for upcoming events.
Why Learn Time-Series Forecasting?
There are several reasons why individuals may want to learn about time-series forecasting:
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Find a path to becoming a Time-Series Forecasting. Learn more at:
OpenCourser.com/topic/yx4lch/time
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Time-Series Forecasting.
A comprehensive guide to forecasting methods, this book covers a wide range of techniques, from simple moving averages to advanced machine learning algorithms.
A seminal work in time-series analysis, this book introduces the famous Box-Jenkins approach, which has become a widely used method for forecasting.
Provides a solid foundation in time-series analysis, with a focus on practical applications using the R programming language.
Focuses on the use of time-series forecasting in economics and finance, covering both theoretical and practical aspects.
Focuses on the practical applications of time-series analysis in business forecasting, providing case studies and real-world examples.
This textbook provides a comprehensive and accessible introduction to time-series analysis, covering both theoretical concepts and practical applications.
This classic text on state space methods for time-series analysis offers a rigorous treatment of the subject, suitable for advanced readers.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/yx4lch/time