Introduction to Time Series and Forecasting, Volume 1

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Springer, Mar 8, 2002 - Business & Economics - 434 pages
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This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences. The book assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This second edition contains detailed instructions on the use of the new totally windows-based computer package ITSM2000. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modeling of financial time series. These models can be fitted using the new version of ITSM. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to non-linear, continuous-time and long-memory models.

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About the author (2002)

Richard Davis is Professor of Chemical Engineering at the University of Minnesota Duluth. He has over two decades experience teaching a variety of courses including computational methods, unit operations of momentum, heat and mass transfer, chemical reactor design, engineering economics, bioprocess engineering, green engineering, and separations. He has BS and PhD degrees in chemical engineering from Brigham Young University and the University of California Santa Barbara, respectively. His current teaching and research interests include process modeling and simulation applied to mineral processing, energy conversion, pollution control, chemical process safety, and environmental management.