Application of wavelet decomposition in time-series forecasting

DOI10.1016/j.econlet.2017.06.010zbMath1396.62221OpenAlexW2639113846MaRDI QIDQ1782354

Keyi Zhang, Ramazan Gençay, M. Ege Yazgan

Publication date: 20 September 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.econlet.2017.06.010

zbMATH Keywords

wavelet decompositioncombining forecastsreconciling forecastshierarchical time series

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40)

Related Items (4)

Improving daily value-at-risk forecasts: the relevance of short-run volatility for regulatory quality assessment ⋮ Wavelet-\(L_2 E\) stochastic volatility models: an application to the water-energy nexus ⋮ Recovering cointegration via wavelets in the presence of non-linear patterns ⋮ Enhancing the predictability of crude oil markets with hybrid wavelet approaches

Uses Software

• forecast
• Forecast

Cites Work

• Unnamed Item
• Optimal combination forecasts for hierarchical time series
• Dynamic prediction pools: an investigation of financial frictions and forecasting performance
• Forecasting inflation using commodity price aggregates
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• When is an aggregate of a time series efficiently forecast by its past?
• An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation
• Fast computation of reconciled forecasts for hierarchical and grouped time series

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