MTU Library Catalogue

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Introduction to time series analysis and forecasting / Douglas C. Montgomery, Cheryl L. Jennings and Murat Kulahci.

By: Montgomery, Douglas C [author].
Contributor(s): Jennings, Cheryl L [author] | Kulahci, Murat [author] | John Wiley & Sons [publisher].
Material type: materialTypeLabelBookSeries: Wiley series in probability and statistics: Publisher: Hoboken, New Jersey : Wiley, [2024]Copyright date: ©2024Edition: Third edition.Description: xi, 697 pages : illustrations.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781394186693 (hardback); 139418669X (hardback).Subject(s): Time-series analysis | ForecastingDDC classification: 519.55
Contents:
Introduction to forecasting -- Statistics background for forecasting -- Regression analysis and forecasting -- Exponential smoothing methods -- Autoregressive integrated moving average (ARIMA) models -- Transfer functions and intervention models -- Other time series analysis and forecasting methods.
Summary: "Forecasting is among the most important topics in many fields, including business and economics, engineering and the physical sciences, and the natural and social sciences. Some common applications include forecasting sales or demand or products or services for management and control of production, inventories and workforce; forecasting inflation rates, stock market indices, unemployment rates and market shares for financial and commercial decision making; and forecasting environmental and demographic variables such as population growth and changes in pollution levels to obtain a better understanding of these phenomena and assess the potential impact of causal factors. The underlying methodology for generating forecasts is the branch of applied statistics that deals with the description and analysis of time oriented data, or time series analysis. The use of computer packages and data sets helps to facilitate otherwise tedious computations"- Provided by publisher.
Holdings
Item type Current library Call number Status Notes Barcode
General lending MTU Bishopstown Library Lending 519.55 (Browse shelf(Opens below)) Available MTU Cork Module STAT8008 - Core reading, MTU Cork Module STAT9005 - Core reading. 00219916
Total holds: 0

Enhanced descriptions from Syndetics:

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.

Includes bibliographical references and index.

Introduction to forecasting -- Statistics background for forecasting -- Regression analysis and forecasting -- Exponential smoothing methods -- Autoregressive integrated moving average (ARIMA) models -- Transfer functions and intervention models -- Other time series analysis and forecasting methods.

"Forecasting is among the most important topics in many fields, including business and economics, engineering and the physical sciences, and the natural and social sciences. Some common applications include forecasting sales or demand or products or services for management and control of production, inventories and workforce; forecasting inflation rates, stock market indices, unemployment rates and market shares for financial and commercial decision making; and forecasting environmental and demographic variables such as population growth and changes in pollution levels to obtain a better understanding of these phenomena and assess the potential impact of causal factors. The underlying methodology for generating forecasts is the branch of applied statistics that deals with the description and analysis of time oriented data, or time series analysis. The use of computer packages and data sets helps to facilitate otherwise tedious computations"- Provided by publisher.

MTU Cork Module STAT8008 - Core reading.

MTU Cork Module STAT9005 - Core reading.

Reviews provided by Syndetics

CHOICE Review

This volume is designed for academic coursework on time series analysis at the advanced undergraduate or first-year graduate level. The reader is assumed to have a background in calculus, probability, and statistics. After a review of some important statistical concepts, authors Montgomery (industrial engineering and statistics, Arizona State Univ.), Jennings (consultant), and Kulahci (informatics and mathematical modeling, Technical Univ. of Denmark) cover three main approaches to time series forecasting: regression, exponential smoothing, and Box-Jenkins ARIMA modeling. The book concludes with a brief chapter introducing more advanced methods. The presentation is focused more on how to apply these forecasting methods rather than on their theoretical development. Many examples and exercises involve the analysis of real data sets. This would be an appropriate resource for use in a first course in time series analysis. It might also be useful as a reference for researchers who want to apply time series analysis to their data sets. Summing Up: Recommended. Upper-division undergraduates, graduate students, researchers, and faculty. B. Borchers New Mexico Institute of Mining and Technology

Author notes provided by Syndetics

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.