Applied time series analysis : a practical guide to modeling and forecasting / Terence C. Mills.

Por: Mills, Terence CTipo de material: TextoTextoDetalles de publicación: London : Academic Press, [2019] Descripción: xiii, 339 pISBN: 9780128131176Tema(s): Análisis de series temporales
Contenidos:
Time series and their features -- Transforming time series -- ARMA models for stationary time series -- ARIMA models for nonstationary time series -- Unit roots, difference and trend stationarity, and fractional differencing -- Breaking and nonlinear trends -- An introduction to forecasting with univariate models -- Unobserved component models, signal extraction, and filters -- Seasonality and exponential smoothing -- Volatility and generalized autoregressive conditional heteroskedastic processes -- Nonlinear stochastic processes -- Transfer functions and autoregressive distributed lag modeling -- Vector autoregressions and Granger causality -- Error corection, spurious regressions, and cointegration -- Vector autoregressions with integrated variables, vector error correction models, and common trends -- Compositional and count time series -- State space models.
Etiquetas de esta biblioteca: No hay etiquetas de esta biblioteca para este título. Inicie sesión para agregar etiquetas.
Valoración
    Valoración media: 0.0 (0 votos)

Bibliografía: (p.: 315-327). - índices

Time series and their features -- Transforming time series -- ARMA models for stationary time series -- ARIMA models for nonstationary time series -- Unit roots, difference and trend stationarity, and fractional differencing -- Breaking and nonlinear trends -- An introduction to forecasting with univariate models -- Unobserved component models, signal extraction, and filters -- Seasonality and exponential smoothing -- Volatility and generalized autoregressive conditional heteroskedastic processes -- Nonlinear stochastic processes -- Transfer functions and autoregressive distributed lag modeling -- Vector autoregressions and Granger causality -- Error corection, spurious regressions, and cointegration -- Vector autoregressions with integrated variables, vector error correction models, and common trends -- Compositional and count time series -- State space models.

No hay comentarios en este titulo.

para aportar su opinión.

Con tecnología Koha