Two-Step Meta-Learning for Time-Series Forecasting Ensemble

Two-Step Meta-Learning for Time-Series Forecasting Ensemble

Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series modeling method is universal to all types of dynamics, forecasting using an ensemble of several methods is often seen as a c...

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Bibliographic Details
Main Authors: Evaldas Vaiciukynas, Paulius Danenas, Vilius Kontrimas, Rimantas Butleris
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Business intelligence
univariate time-series model
forecasting ensemble
meta-learning
random forest
M4 competition
Online Access:https://ieeexplore.ieee.org/document/9410467/

Internet

https://ieeexplore.ieee.org/document/9410467/

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