Reinforcement Learning-Guided Channel Selection Across Time for Multivariate Time Series Classification

The promising results of machine learning in time series classification, along with the rise in sensor data-driven use cases, have led to the increasing deployment of models in IoT environments, on edge devices. Since these devices are typically resource constrained, they cannot always execute large and complex models, so they often offload (part of) their tasks to remotely located models. This synergy however introduces the need to transfer a large amount of sensor data to the cloud, which can be detrimental to bandwidth cost and inference speed of the application, and energy utilization of the device. Although techniques such as early classification can limit the data that has to be transferred, there are still unexplored opportunities when it comes to input filtering. A recent versatile early-exit framework, extending early classification and adapting it to multivariate time series, has investigated this potential. In this work, we propose a variation of this method, creating a more flexible, reinforcement learning-enabled framework that can adapt the input variables (channels) considered for classification across time, aiming for maximizing accuracy while minimizing the input data necessary. Extensive testing on synthetic data and real datasets shows that our method can, in multiple cases, achieve better accuracy for a similar percentage of input filtering, both compared to the baseline framework, as well as to the conventional early classification approach.