Temporal Conditional VAE for Distributional Drift Adaptation in Multivariate Time Series. (arXiv:2209.00654v2 [cs.LG] UPDATED)

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 the distribution drift and
degrade the forecasting performance over time. Existing methods address
distribution drift via adapting to the latest arrived data or self-correcting
per the meta knowledge derived from future data. Despite their great success in
MTS forecasting, these methods hardly capture the intrinsic distribution
changes especially from a …

arxiv series temporal time series