Neural Architecture Search for global multi-step Forecasting of Energy Production Time Series

• URL: http://arxiv.org/abs/2511.00035v1
• Date: Mon, 27 Oct 2025 15:56:37 GMT
• Title: Neural Architecture Search for global multi-step Forecasting of Energy Production Time Series
• Authors: Georg Velev, Stefan Lessmann,
• Abstract summary: We design a neural architecture search (NAS)-based framework for the automated discovery of time series models.<n>In particular, we introduce a search space consisting only of efficient components, which can capture distinctive patterns of energy time series.<n>Results show that NAS outperforms state-of-the-art techniques, such as Transformers, in terms of both efficiency and accuracy.
• Score: 4.605677844197738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dynamic energy sector requires both predictive accuracy and runtime efficiency for short-term forecasting of energy generation under operational constraints, where timely and precise predictions are crucial. The manual configuration of complex methods, which can generate accurate global multi-step predictions without suffering from a computational bottleneck, represents a procedure with significant time requirements and high risk for human-made errors. A further intricacy arises from the temporal dynamics present in energy-related data. Additionally, the generalization to unseen data is imperative for continuously deploying forecasting techniques over time. To overcome these challenges, in this research, we design a neural architecture search (NAS)-based framework for the automated discovery of time series models that strike a balance between computational efficiency, predictive performance, and generalization power for the global, multi-step short-term forecasting of energy production time series. In particular, we introduce a search space consisting only of efficient components, which can capture distinctive patterns of energy time series. Furthermore, we formulate a novel objective function that accounts for performance generalization in temporal context and the maximal exploration of different regions of our high-dimensional search space. The results obtained on energy production time series show that an ensemble of lightweight architectures discovered with NAS outperforms state-of-the-art techniques, such as Transformers, as well as pre-trained forecasting models, in terms of both efficiency and accuracy.

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