A huge range of fields rely on forecasts of how certain variables and causal factors will affect future outcomes, from product sales to inflation rates to demographic changes. Time series analysis is the branch of applied statistics which generates forecasts, and its sophisticated use of time oriented data can vastly impact the quality of crucial predictions. The latest computing and statistical methodologies are constantly being sought to refine these predictions and increase the confidence with which important actors can rely on future outcomes.

Time Series Analysis and Forecasting presents a comprehensive overview of the methodologies required to produce these forecasts with the aid of time-oriented data sets. The potential applications for these techniques are nearly limitless, and this foundational volume has now been updated to reflect the most advanced tools. The result, more than ever, is an essential introduction to a core area of statistical analysis.

Readers of the third edition of Time Series Analysis and Forecasting will also find:

Updates incorporating JMP, SAS, and R software, with new examples throughout
Over 300 exercises and 50 programming algorithms that balance theory and practice
Supplementary materials in the e-book including solutions to many problems, data sets, and brand-new explanatory videos covering the key concepts and examples from each chapter.

Time Series Analysis and Forecasting is ideal for graduate and advanced undergraduate courses in the areas of data science and analytics and forecasting and time series analysis. It is also an outstanding reference for practicing data scientists.

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Yes, you can access Introduction to Time Series Analysis and Forecasting by Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Wiley

Year

2024

ISBN

9781394186716

Edition

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

Cover
Title Page
Copyright
Contents
Preface
About the Companion Website
Chapter 1 Introduction to Time Series Analysis and Forecasting
Chapter 2 Statistics Background for Time Series Analysis And Forecasting
Chapter 3 Regression Analysis and Forecasting
Chapter 4 Exponential Smoothing Methods
Chapter 5 Autoregressive Integrated Moving Average (ARIMA) Models
Chapter 6 Transfer Functions and Intervention Models
Chapter 7 Other Time Series Analysis and Forecasting Methods
Appendix A Statistical Tables
Appendix B Data Sets for Exercises
Appendix C Introduction to R
Bibliography
Index
Wiley Series in Probability and Statistics
EULA