Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods addr...

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Bibliographic Details

Published in:	IEEE transaction on neural networks and learning systems Vol. 36; no. 4; pp. 7287 - 7301
Main Authors:	He, Hui, Zhang, Qi, Yi, Kun, Shi, Kaize, Niu, Zhendong, Cao, Longbing
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.04.2025
Subjects:	Adaptation models Data models Distributional drift Forecasting Gaussian distribution multivariate time series (MTS) Predictive models Time series analysis Training variational autoencoder (VAE)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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