AE-DIL: a double incremental learning algorithm for non-stationary time series prediction via adaptive ensemble
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Publication:6124686
DOI10.1016/j.ins.2023.03.141OpenAlexW4362589445MaRDI QIDQ6124686
Publication date: 28 March 2024
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2023.03.141
incremental learningensemble learningstatistical hypothesis testnon-stationary time series predictionself-adaptive sliding window
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
- A generalization of the Sherman-Morrison-Woodbury formula
- Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting
- Time Series Analysis: Forecasting and Control, 5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978-1-118-67502-1
- Generalization Bounds for Time Series Prediction with Non-stationary Processes
- Distribution of the Estimators for Autoregressive Time Series With a Unit Root
- Machine Learning
- Advances in Artificial Intelligence – SBIA 2004