  Due to the sweeping digitalization of processes, increasingly vast amounts of
time series data are being produced. Accurate classification of such time
series facilitates decision making in multiple domains. State-of-the-art
classification accuracy is often achieved by ensemble learning where results
are synthesized from multiple base models. This characteristic implies that
ensemble learning needs substantial computing resources, preventing their use
in resource-limited environments, such as in edge devices. To extend the
applicability of ensemble learning, we propose the LightTS framework that
compresses large ensembles into lightweight models while ensuring competitive
accuracy. First, we propose adaptive ensemble distillation that assigns
adaptive weights to different base models such that their varying
classification capabilities contribute purposefully to the training of the
lightweight model. Second, we propose means of identifying Pareto optimal
settings w.r.t. model accuracy and model size, thus enabling users with a space
budget to select the most accurate lightweight model. We report on experiments
using 128 real-world time series sets and different types of base models that
justify key decisions in the design of LightTS and provide evidence that
LightTS is able to outperform competitors.
