Introduction to Time Series Analysis and Forecasting

ISBN-10: 1118745116
ISBN-13: 9781118745113
Edition: 2nd 2015
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Description: Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need  More...

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Book details

Edition: 2nd
Copyright year: 2015
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/9/2015
Binding: Hardcover
Pages: 648
Size: 6.50" wide x 10.00" long x 1.25" tall
Weight: 2.530
Language: English

Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting.  Introduction to Time Series Analysis and Forecasting, Second Edition presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.  This new edition has been updated with JMP, SAS, and R code and examples throughout (all of which the Minitab utilization from the prior edition).  Also, new chapter coverage on frequency domains and spatial temporal data analysis have been addition, in addition to transfer function and intervention model examples.  The authors have completely reviewed the book to include new examples from diverse disciplines and improve clarity, both in an effort to improve understanding.  Chapter coverage includes: introduction to forecasting; statistics background for forecasting; regression analysis and forecasting; exponential smoothing methods; autoregressive integrated moving average (ARIMA) models; transfer function and intervention models; frequency domains; spatial temporal data analysis; and survey of other forecasting methods.

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