Adaptive Latent Decomposition for Domain Generalization in Time Series Forecasting

Time series forecasting is essential in many real-world applications, yet developing models that generalize well to unseen and related domains – such as forecasting web traffic on new websites/platforms or predicting e-commerce demand in new regions – remains a big challenge. Prior work addressing this problem, known as domain generalization, focuses on identifying common patterns but often overlooks the complex characteristics of time series data and fails to use the available information from test samples of unseen domains. We propose a novel approach, adaptive latent decomposition (ALD) for domain generalization in time series forecasting, which consists of a decomposed variational autoencoder (VAE) and an adaptive inference mechanism to improve predictive performance in unseen domains. The decomposed VAE involves a learnable kernel-selection mechanism that learns latent variables of decomposed components of time series, i.e., trend-cyclical and seasonal components. These latent variables capture hidden temporal dependencies in time series data, allowing forecasting models to learn general patterns from various seen domains. The adaptive inference mechanism bridges the gap between seen and unseen domains with a sample-wise optimization strategy specifically designed for time series forecasting. ALD builds a latent variables-aware pre-trained model and tailors it for each test sample, improving the generalization on unseen test domains. We validate ALD across six real-world datasets, from online behavior to complex temporal systems, demonstrating its superior generalization performance compared to state-of-the-art methods.