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Sliding simulation: a new approach to time series forecasting

Author: Makridakis, Spyros INSEAD Area: Technology and Operations ManagementIn: Management Science, vol. 36, no. 4, May 1990 Language: EnglishDescription: p. 505-512.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: This paper proposes a new approach to time series forecasting based upon three premises. First, a model is selected not by how well it fits historical data but on its ability to accurately predict out-of-sample information. Third, models/methods are optimised for each forecasting horizon separately, making it possible to have different models/methods to predict each of the m horizons. This approach outperforms the best method of the M-Competition by a large margin when tested empirically with the 111 series subsample of the M-Competition data
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This paper proposes a new approach to time series forecasting based upon three premises. First, a model is selected not by how well it fits historical data but on its ability to accurately predict out-of-sample information. Third, models/methods are optimised for each forecasting horizon separately, making it possible to have different models/methods to predict each of the m horizons. This approach outperforms the best method of the M-Competition by a large margin when tested empirically with the 111 series subsample of the M-Competition data

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