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Forecasting principles and applications / Stephen A. DeLurgio.

By: Material type: TextTextLanguage: English Series: Statistic & probability seriesPublication details: Boston : Irwin McGraw-Hill, 1998.Description: xxviii, 802 páginas : tablas, gráficas ; 26 cmContent type:
  • Texto
Media type:
  • Sin mediación
Carrier type:
  • Volumen
ISBN:
  • 0256134332
Subject(s): DDC classification:
  • 003.2015195  D358f  21
Other classification:
  • G17
Contents:
Part I: Foundations of forecasting: 1. Planning and forecasting ; 2.Statistical fundamentals for forecasting ; 3. Simple linear regression analuysis -- Part II: Univariate methods ; 5. Decomposition methods and seasonal indexes ; 6. Trend-seasonal and holt-winters smoothing -- Part III: Univariate arima methods: 7. Univariate ARIMA models: introduction ; ARIMA applications ; ARIMA forescast intervals -- Part IV: Multivariate/causal methods: 10. Multiple regression of time series ; 11. Econometric methods ; 12. ARIMA Intervention analysis ; 13. Multivariate ARIMA transfer functions -- Part V: Cyclical, qualitative, and artificial intelligence methods: 14. Cyclical forecasting methods ; 15. Technological and quialitative forecasting methods: long-term forecasting ; 16 Artificial neural networks, expert systems, and genetic algorithms ; Part VI: Combining, validations, and combining methods ; 18. Method characteristics, accuracy, and data sources.
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Holdings
Item type Home library Call number Status Notes Date due Barcode Item holds
LIBRO FISICO Biblioteca Principal 003.2015195 D358f (Browse shelf(Opens below)) Available Mantener en colección. 29004019011906
Total holds: 0

Incluye bibliografías e índice

Part I: Foundations of forecasting: 1. Planning and forecasting ; 2.Statistical fundamentals for forecasting ; 3. Simple linear regression analuysis -- Part II: Univariate methods ; 5. Decomposition methods and seasonal indexes ; 6. Trend-seasonal and holt-winters smoothing -- Part III: Univariate arima methods: 7. Univariate ARIMA models: introduction ; ARIMA applications ; ARIMA forescast intervals -- Part IV: Multivariate/causal methods: 10. Multiple regression of time series ; 11. Econometric methods ; 12. ARIMA Intervention analysis ; 13. Multivariate ARIMA transfer functions -- Part V: Cyclical, qualitative, and artificial intelligence methods: 14. Cyclical forecasting methods ; 15. Technological and quialitative forecasting methods: long-term forecasting ; 16 Artificial neural networks, expert systems, and genetic algorithms ; Part VI: Combining, validations, and combining methods ; 18. Method characteristics, accuracy, and data sources.

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