Introduction to Time Series Analysis and Forecasting
Bring the latest statistical tools to bear on predicting future variables and outcomes
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.
Douglas C. Montgomery, PhD, is Regents Professor of Industrial Engineering and ASU Foundation Professor of Engineering at Arizona State University, USA. He holds a PhD in Engineering from Virginia Tech and has researched and published extensively on industrial statistics and experimental design.
Cheryl Jennings, PhD, is Associate Teaching Professor at Arizona State University. She has decades of industrial experience in manufacturing and financial services, and has taught undergraduate and graduate courses on modeling and analysis, performance management, process control, and related subjects.
Murat Kulahci, PhD, is Professor of Industrial Statistics at the Technical University of Denmark and Professor at the Luleå University of Technology, Sweden. He holds a PhD in Industrial Engineering from the University of Wisconsin, Madison. He has published widely on time series analysis, experimental design, process monitoring and related subjects.