A Wave Is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series

Time series analysis is a fundamental data mining task that has made encouraging progress in many real-world scenarios. Supervised training methods based on empirical risk minimization have proven their effectiveness on specific tasks and datasets. However, the acquisition of well-annotated data is costly, and a large amount of unlabeled series data is under-utilized. Due to distributional shifts across various domains and different patterns of interest across multiple tasks. The problem of cross-domain multi-task migration remains a significant challenge. To address these problems, this article proposes a novel cross-domain approach based on Wave Quantization for Time Series (termed as WQ4TS), which can be combined with any advanced time series model and applied to diverse downstream tasks. Specifically, we transfer the data from different domains into a common spectral latent space and enable the model to learn the temporal pattern knowledge of different domains directly from the common space and utilize it for the inference of downstream tasks, thereby mitigating the challenge of heterogeneous migration. The establishment of spectral latent space brings at least three benefits, cross-domain migration capability thus adapting to zero- and few-shot scenarios without relying on priori knowledge, general compatible cross-domain framework without changing the existing model structure, and robust modeling capability thus achieving SOTA results in multiple downstream tasks. To demonstrate the effectiveness of the proposed approach, we conduct extensive experiments including three important tasks: forecasting, imputation, and classification. And three common real-world scenarios are simulated: full-data, few-shot, and zero-shot. The proposed WQ4TS achieves the best performance on 87.5% of all tasks. Concretely, WQ4TS achieved 25.8% and 44.1% improvements in MSE metric for few-shot and zero-shot forecasting tasks, respectively, and demonstrated excellent 24.9% increase in average accuracy on few-shot classification tasks. The source codes of WQ4TS are publicly available on https://github.com/Xiang-Kai/WQ4TS .