Time Series Analysis and Forecasting by Example (Wiley Desktop Editions) by S?ren Bisgaard (Author), Murat Kulahci (Author)

Time Series Analysis and Forecasting by Example (Wiley Desktop Editions) by S?ren Bisgaard (Author), Murat Kulahci (Author)
Item# 13020411736
Retail price: US$125.00
Sale price: US$8.00

all items in this store are to be sent to your email within 24 hours after cleared payment. PDF eBooks are sent to you as email attachments. as for mp3 audiobook, a download link from ONEDRIVE will be sent to your email for you to download.

Please Read Before Your Purchase!!!

1. Please Read Before Your Purchase!!!

2. Shipping & Delivery: Send to you by E-mail within 24 Hours after cleared payment. Immediately Arrival!!!

3. Shipping ( by email) + Handling Fee = US$0.00 ( Promotional Period)

4. Time-Limited Offer, Order Fast.

*************************************************************************







Time Series Analysis and Forecasting by Example (Wiley Desktop Editions) by S?ren Bisgaard (Author), Murat Kulahci (Author)



Publisher: Wiley; 1 edition (August 9, 2011)

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications.

The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including:

Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets.

With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

Review “It is a suitable text for courses on time series analysis at the (upper) undergraduate and graduate level. It can also serve as a guide for practitioners and researchers who carry out time series analysis in engineering, business and economics.” (Zentralblatt MATH, 2012)

"Time Series Analysis and Forecasting by Example is well recommended as a great introductory book for students transitioning from general statistics to time series as well as a good source book for intermediate level time series model builders." (Book Pleasures, 2012) "They set out to provide an introduction that is easy to understand and use, and that draws heavily from examples to demonstrate the principles and techniques." (Book News, 1 October 2011)

From the Back Cover An intuition-based approach enables you to master time series analysis with ease

Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications.

The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including:

Graphical tools in time series analysis

Procedures for developing stationary, non-stationary, and seasonal models

How to choose the best time series model

Constant term and cancellation of terms in ARIMA models

Forecasting using transfer function-noise models

The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures along with instructions for using statistical software packages such as SAS®, JMP®, Minitab®, SCA, and R. A related website features PowerPoint® slides that accompany each chapter as well as the book's data sets.

With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate level. It also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.