Introduction Deep learning has attracted considerable attention for its near-human ability in a variety of complex problems such as image recognition, playing games, and recently conversational AI through large language models. Each of these applications requires unimaginable volumes of data and computational resources beyond the reach of all but the richest companies. This resource hungry nature, coupled with the huge hype that accompanies any deep-learning application, makes it challenging to gain a realistic assessment of their real-world potential for less demanding use-cases, such as scientific time-series modelling.