  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 an example close
to its frequency-based neighborhood -- 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 8.4% (precision) in
challenging one-to-many settings (e.g., fine-tuning an EEG-pretrained model for
either hand-gesture recognition or mechanical fault prediction), reflecting the
breadth of scenarios that arise in real-world applications. Code and datasets:
https://github.com/mims-harvard/TFC-pretraining.
