Transferable Time-Series Forecasting Under Causal Conditional Shift.

Area of Science:

• Machine Learning
• Data Science
• Time-Series Analysis

Background:

• Semi-supervised domain adaptation for time-series forecasting is an underexplored but practical problem.
• Existing methods for static data fail to capture complex dependencies in time-series data, such as data offset, time lags, and distribution shifts.
• These limitations hinder accurate forecasting across different data domains.

Purpose of the Study:

• To address the challenges in semi-supervised domain adaptation for time-series forecasting.
• To propose a novel end-to-end model that accounts for domain-specific conditional dependencies.
• To improve the accuracy and interpretability of time-series forecasts in cross-domain scenarios.

Main Methods:

• Analysis of variational conditional dependencies in time-series data.
• Formulation of the causal conditional shift assumption based on stable causal structures across domains.
• Development of an end-to-end model incorporating causal generation processes for time-series data.
• Discovery of Granger-Causal structures within cross-domain data.

Main Results:

• The proposed method effectively addresses semi-supervised domain adaptation for time-series forecasting.
• Demonstrated ability to discover Granger-Causal structures and perform accurate, interpretable cross-domain forecasting.
• Theoretical analysis bounds generalization error by empirical risks and causal structure discrepancies.
• Experimental validation on synthetic and real-world datasets confirms the method's effectiveness.

Conclusions:

• The proposed causal-based approach significantly advances semi-supervised domain adaptation for time-series forecasting.
• The method offers a robust solution for handling domain shifts by exploiting stable causal relationships.
• Future work can explore further applications of causal inference in time-series modeling.