Résumé :
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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. Some of the key mathematical results are stated without prooof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-atationarytime series are developed in detail and numerous exercises, many of whic make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectyral analysis. Additional topics include harmonic regression, the Burg and Hanna-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorith, generalized state-space models with applications to time series of count data, exponentail smoothing, the Holt-Winters and ARAR forescasting 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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