Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

• URL: http://arxiv.org/abs/2206.08496v1
• Date: Fri, 17 Jun 2022 00:45:04 GMT
• Title: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
• Authors: Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik
• Abstract summary: Methods need to accommodate target domains with different temporal dynamics. Time-frequency consistency (TF-C) is desirable for pre-training. Experiments show TF-C outperforms baselines by 15.4% (F1 score) on average in one-to-one settings.
• Score: 19.1862172442857
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate these shifts, most methods need examples directly from the target domain, making them suboptimal for pre-training. To address this challenge, methods need to accommodate target domains with different temporal dynamics and be capable of doing so without seeing any target examples during pre-training. Relative to other modalities, in time series, we expect that time-based and frequency-based representations of the same example are located close together in the time-frequency space. To this end, we posit that time-frequency consistency (TF-C) -- embedding a time-based neighborhood of a particular example close to its frequency-based neighborhood and back -- is desirable for pre-training. Motivated by TF-C, we define a decomposable pre-training model, where the self-supervised signal is provided by the distance between time and frequency components, each individually trained by contrastive estimation. We evaluate the new method on eight datasets, including electrodiagnostic testing, human activity recognition, mechanical fault detection, and physical status monitoring. Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15.4% (F1 score) on average in one-to-one settings (e.g., fine-tuning an EEG-pretrained model on EMG data) and by up to 8.4% (F1 score) in challenging one-to-many settings, reflecting the breadth of scenarios that arise in real-world applications. The source code and datasets are available at https: //anonymous.4open.science/r/TFC-pretraining-6B07.

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