Application of wavelet decomposition in time-series forecasting
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science Sa
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Observed time series data can exhibit different components, such as trends, seasonality, and jumps, which are characterized by different coefficients in their respective data generating processes. Therefore, fitting a given time series model to aggregated data can be time consuming and may lead to a loss of forecasting accuracy. In this paper, coefficients for variable components in estimations are generated based on wavelet-based multiresolution analyses. Thus, the accuracy of forecasts based on aggregate data should be improved because the constraint of equality among the model coefficients for all data components is relaxed. (C) 2017 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Wavelet Decomposition, Combining Forecasts, Reconciling Forecasts, Hierarchical Time Series, Combination Forecasts, Aggregate
Kaynak
Economics Letters
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
158
