A wavelet-based method for time series forecasting

Gabriela Dominguez, Miguel Guevara, Marcelo Mendoza, JUAN FRANCISCO ZAMORA OSORIO

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

Usually time series are controlled by two or more data generative processes which display changes over time. Each one of these processes may be described by different models. In practice, the observed data is an aggregated view of the processes, fact which limits the effectivity of any model selection procedure. In many occasions, the data generative processes may be separated by using spectral analysis methods, reconstructing a specific part of the data by filtering bands. Then, a filtered version of the series may be forecasted, by using proper model selection procedures. In this article we explore the use of forecasting methods in the wavelet space. To do this, we decompose the time series into a number of scale time sequences by applying a discrete wavelet transform. By fitting proper ARIMA models at each resolution level, a forecasting step is conducted. Then, by applying the inverse wavelet transform, we reconstruct forecasted time series. Experimental results show the feasibility of the proposed approach.

Idioma originalInglés
Título de la publicación alojadaProceedings - 31st International Conference of the Chilean Computer Science Society, SCCC 2012
EditorialIEEE Computer Society
Páginas91-94
Número de páginas4
ISBN (versión impresa)9781479929375
DOI
EstadoPublicada - 1 ene. 2013
Evento31st International Conference of the Chilean Computer Science Society, SCCC 2012 - Valparaiso, Chile
Duración: 12 nov. 201216 nov. 2012

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (versión impresa)1522-4902

Conferencia

Conferencia31st International Conference of the Chilean Computer Science Society, SCCC 2012
País/TerritorioChile
CiudadValparaiso
Período12/11/1216/11/12

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