Applied Time Series: Analysis and Forecasting provides the theories methods and tools for necessary modeling and forecasting of time series. It includes a complete theoretical development of univariate time series models with each step demonstrated with an analysis of real time data series. The result is clear presentation, quantified subjective judgment derived from selected models applied to time series observations. Utilizing real time series to demonstrate theoretical Approaches, the author explores diverse aspects of time series, including how to identify structure, explain observed behaviour, model structures and behaviours, and interpret analysis to make informed forecast. The book also illustrates concepts such as components decomposition, fundamental model form such as trends cycles seasonality and practical requirement.

Preface / Abbreviations / Scope of the Book / Analysis of Time Series / Classical Time Series Decomposition / Exponential Smoothing Method / Stationary & Nonstationary Time Series / Stationary Stochastic Processes / The Box-Jenkins ARIMA Methodology / Model Identification and Diagnostics Checking / Applications of Box-Jenkins Methodology / Recursive Estimation of Time Series Models / Recursive Estimation and Time-Varying Parameter Models / Implementation of Recursive Methods in Excel Spread Sheet / Appendix / Glossary of Forecasting Terms / References / Index.

Undergraduate – Postgraduate Students, Researchers & Professionals within business, statistics, economics, engineering, and operations research program