Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models

[EN] The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the pred...

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Detalles Bibliográficos
Autores: TRULL DOMÍNGUEZ, OSCAR|||0000-0003-2896-8606, García-Díaz, J. Carlos|||0000-0002-5559-7110, Troncoso, Alicia
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/162104
Acceso en línea:https://riunet.upv.es/handle/10251/162104
Access Level:acceso abierto
Palabra clave:Forecasting
Multiple seasonal periods
Holt-Winters
Initialization
ESTADISTICA E INVESTIGACION OPERATIVA
Descripción
Sumario:[EN] The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt-Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt-Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt-Winters models with an additive and multiplicative trend is provided.