For many years Recurrent Neural Networks (RNNs) achieved state-of-the-art (SoTA) results in time series analysis, but their large computational complexity makes them ill-suited to be deployed on microcontrollers and, in general, on resource-constrained edge devices. A more viable alternative, towards the efficient deployment of time series related tasks is represented by Temporal Convolutional Networks (TCNs), a particular class of Convolutional Neural Networks (CNNs), achieving comparable results to SoTA RNNs. TCNs offer many advantages from a computational standpoint, resulting in a more hardware-friendly alternative to RNNs. Nevertheless, the optimized deployment of a TCN on a microcontroller-based edge device still requires a careful and time-consuming hand-tuning of the model's hyper-parameters.