  Automated machine learning (AutoML) streamlines the creation of ML models.
While most methods select the "best" model based on predictive quality, it's
crucial to acknowledge other aspects, such as interpretability and resource
consumption. This holds particular importance in the context of deep neural
networks (DNNs), as these models are often perceived as computationally
intensive black boxes. In the challenging domain of time series forecasting,
DNNs achieve stunning results, but specialized approaches for automatically
selecting models are scarce. In this paper, we propose AutoXPCR - a novel
method for automated and explainable multi-objective model selection. Our
approach leverages meta-learning to estimate any model's performance along PCR
criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource
demand. Explainability is addressed on multiple levels, as our interactive
framework can prioritize less complex models and provide by-product
explanations of recommendations. We demonstrate practical feasibility by
deploying AutoXPCR on over 1000 configurations across 114 data sets from
various domains. Our method clearly outperforms other model selection
approaches - on average, it only requires 20% of computation costs for
recommending models with 90% of the best-possible quality.
