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

Area of Science:

• Machine Learning
• Time Series Analysis
• Artificial Intelligence

Background:

• Real-world multivariate time series (MTS) data exhibit nonstationary behavior, leading to distribution drift.
• Existing MTS forecasting models often degrade in performance due to their inability to adapt to distribution drift.
• Current methods for handling distribution drift focus on data adaptation or future-data-informed self-correction, neglecting intrinsic distributional changes.

Purpose of the Study:

• To propose a novel framework, the Temporal Conditional Variational Autoencoder (TCVAE), for modeling dynamic distributional dependencies in MTS.
• To capture intrinsic distribution changes from a distributional perspective, enhancing forecasting accuracy.
• To leverage latent variables through a temporal conditional distribution for improved MTS forecasting.

Main Methods:

• Developed a Temporal Hawkes Attention (THA) mechanism to represent temporal factors influencing latent variable priors.
• Employed a Gated Attention Mechanism (GAM) to dynamically adjust Transformer-based encoder/decoder structures in response to distribution changes.
• Introduced Conditional Continuous Normalization Flow (CCNF) to transform Gaussian priors into complex, form-free distributions for flexible inference.

Main Results:

• TCVAE demonstrated superior robustness and effectiveness compared to state-of-the-art MTS forecasting baselines across six real-world datasets.
• The model successfully captures dynamic distributional dependencies over time.
• Experiments validated the model's ability to handle distribution drift effectively.

Conclusions:

• The proposed TCVAE framework offers a significant advancement in MTS forecasting by explicitly modeling temporal distributional changes.
• TCVAE provides a robust and effective solution for forecasting nonstationary time series data.
• The framework's applicability is further supported by case studies and visualizations in real-world scenarios.