Introduction to Time Series Analysis and Forecasting - Hardcover

Lavra Filipek

Hardcover

ISBN 10: 1681171910 ISBN 13: 9781681171913

Publisher: Scitus Academics LLC, 2016

Synopsis

A time series is a collection of data recorded over a period of timeweekly, monthly, quarterly, or yearly. Forecasting the level of sales, both short-term and long-term, is practically dictated by the very nature of business organizations. Competition for the consumers dollar, stress on earning a profit for the stockholders, a desire to procure a larger share of the market, and the ambitions of executives are some of the prime motivating forces in business. Thus, a forecast is necessary to have the raw materials, production facilities, and staff available to meet the projected demand. Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic. Analyzing time oriented data and forecasting future values of a time series are among the most important problems that analysis face in many fields ranging from finance and economics to managing production operations. The emphasis of this book is on time series analysis and forecasting. This book is intended for practitioners who make real world forecasts. Time series analysis has got attention of many researches from different fields, such as business administration, economics, public finances. Forecasting is an important activity in economics, commerce, marketing and various branches of science. This book, Introduction to Time Series Analysis and Forecasting, is concerned with forecasting methods based on the use of time-series analysis. It is primarily intended as a reference source for practitioners and researchers in forecasting, who could, for example, be statisticians, econometricians, operational researchers, management scientists or decision scientists.

Edition details

• Publisher: Scitus Academics LLC
• Publication date: 2016
• Language: English
• ISBN 10: 1681171910
• ISBN 13: 9781681171913
• Binding: Hardcover
• Number of pages: 256