  Domain adaptation (DA) offers a valuable means to reuse data and models for
new problem domains. However, robust techniques have not yet been considered
for time series data with varying amounts of data availability. In this paper,
we make three main contributions to fill this gap. First, we propose a novel
Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that
significantly improves accuracy and training time over state-of-the-art DA
strategies on real-world sensor data benchmarks. By utilizing data from
multiple source domains, we increase the usefulness of CoDATS to further
improve accuracy over prior single-source methods, particularly on complex time
series datasets that have high variability between domains. Second, we propose
a novel Domain Adaptation with Weak Supervision (DA-WS) method by utilizing
weak supervision in the form of target-domain label distributions, which may be
easier to collect than additional data labels. Third, we perform comprehensive
experiments on diverse real-world datasets to evaluate the effectiveness of our
domain adaptation and weak supervision methods. Results show that CoDATS for
single-source DA significantly improves over the state-of-the-art methods, and
we achieve additional improvements in accuracy using data from multiple source
domains and weakly supervised signals. Code is available at:
https://github.com/floft/codats
