According to the experimental evaluation conducted, suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for approximately half (130/262) of the evaluated time series.
Jan 15, 2019
Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time series.
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Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time ...
[PDF] BENCHMARKING NONSTATIONARY TIME SERIES PREDICTION ... - EIC
eic.cefet-rj.br › uploads › 2018/11
A subset of the reviewed transformation methods is compared through an experimental evaluation using benchmark datasets from time series prediction competitions ...
Nonstationary time series transformation methods: An experimental review. Author(s): Rebecca Salles , Kele Belloze , Fabio Porto , Pedro H. Gonzalez ...
Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time series.
This paper provides a review and experimental analysis of methods for transformation of nonstationary time series. The focus of this work is to provide a ...
Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time series.
Oct 31, 2022 · The paper introduces a transformer-based method for non-stationary time series forecasting. This research addresses a clear need, as ...
A systematic framework for nonstationary time series prediction that enables benchmarking data transformation methods and MLM. • A benchmarking and experimental ...