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Modelling trigonometric seasonal components for monthly economic time series
Hindrayanto, Irma; Aston, John A. D.; Koopman, Siem Jan; … - In: Applied economics 45 (2013) 19/21, pp. 3024-3034
Persistent link: https://www.econbiz.de/10010192327
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Monte Carlo maximum likelihood estimation for generalized long-memory time series models
Mesters, G.; Koopman, Siem Jan; Ooms, Marius - 2011
Persistent link: https://www.econbiz.de/10009720782
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An hourly periodic state space model for modelling French national electricity load
Dordonnat, V.; Koopman, Siem Jan; Ooms, Marius; … - 2008
We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...
Persistent link: https://www.econbiz.de/10011373810
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Monte Carlo maximum likelihood estimation for generalized long-memory time series models
Mesters, G.; Koopman, Siem Jan; Ooms, Marius - In: Econometric reviews 35 (2016) 1/4, pp. 659-687
Persistent link: https://www.econbiz.de/10011550112
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Modeling trigonometric seasonal components for monthly economic time series
Hindrayanto, Irma; Aston, John A.D.; Koopman, Siem Jan; … - 2010
The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this paper we explore a generalisation of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal...
Persistent link: https://www.econbiz.de/10011379642
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An Hourly Periodic State Space Model for Modelling French National Electricity Load
Dordonnat, V.; Koopman, S.J.; Ooms, M.; Dessertaine, A.; … - 2008
We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...
Persistent link: https://www.econbiz.de/10010325676
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An Hourly Periodic State Space Model for Modelling French National Electricity Load
Dordonnat, V.; Koopman, S.J.; Ooms, M.; Dessertaine, A.; … - Tinbergen Instituut - 2008
We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...
Persistent link: https://www.econbiz.de/10011256898
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An Hourly Periodic State Space Model for Modelling French National Electricity Load
Dordonnat, V.; Koopman, S.J.; Ooms, M.; Dessertaine, A.; … - Tinbergen Institute - 2008
We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...
Persistent link: https://www.econbiz.de/10005144435
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Modeling Trigonometric Seasonal Components for Monthly Economic Time Series
Hindrayanto, Irma; Aston, John A.D.; Koopman, Siem Jan; … - 2010
The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this paper we explore a generalisation of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal...
Persistent link: https://www.econbiz.de/10010325881
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Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling
Dordonnat, Virginie; Koopman, Siem Jan; Ooms, Marius - In: Computational Statistics & Data Analysis 56 (2012) 11, pp. 3134-3152
A dynamic multivariate periodic regression model for hourly data is considered. The dependent hourly univariate time series is represented as a daily multivariate time series model with 24 regression equations. The regression coefficients differ across equations (or hours) and vary...
Persistent link: https://www.econbiz.de/10010617652