Self-supervised Autoregressive Domain Adaptation for Time Series Data

TL;DR

This paper introduces SLARDA, a novel self-supervised autoregressive framework for unsupervised domain adaptation in time series data, addressing limitations of existing methods by leveraging temporal dependencies and class-wise alignment.

Contribution

SLARDA is the first to incorporate self-supervised forecasting and autoregressive domain alignment specifically for time series domain adaptation.

Findings

SLARDA outperforms state-of-the-art methods on three real-world datasets.

The approach effectively captures temporal dependencies during domain adaptation.

Significant improvements in transferability and class-wise alignment are demonstrated.

Abstract

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on large-scale dataset (i.e., ImageNet) for the source pretraining, which is not applicable for time-series data. Second, they ignore the temporal dimension on the feature space of the source and target domains during the domain alignment step. Last, most of prior UDA methods can only align the global features without considering the fine-grained class distribution of the target domain. To address these limitations, we propose a Self-supervised Autoregressive Domain Adaptation (SLARDA) framework. In particular, we first design a self-supervised learning module that utilizes forecasting as an auxiliary task to improve the transferability of the source features. Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment. Finally, we develop an ensemble teacher model to align the class-wise distribution in the target domain via a confident pseudo labeling approach. Extensive experiments have been conducted on three real-world time series applications with 30 cross-domain scenarios. Results demonstrate that our proposed SLARDA method significantly outperforms the state-of-the-art approaches for time series domain adaptation.