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

arXiv: https://arxiv.org/abs/2511.00035 · HTML: https://mlsanity.com/paper/2511.00035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 82 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://mlsanity.com/paper/2303.06053/) (2023)
- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [Sundial: A Family of Highly Capable Time Series Foundation Models](https://mlsanity.com/paper/2502.00816/) (2025)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Stabilizing Differentiable Architecture Search via Perturbation-based Regularization](https://mlsanity.com/paper/2002.05283/) (2020)
- [MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing](https://mlsanity.com/paper/2302.04501/) (2023)
- [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://mlsanity.com/paper/2310.20496/) (2023)
- [Markov Decision Processes with Recursive Risk Measures](https://mlsanity.com/paper/2010.07220/) (2020)
- [Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting](https://mlsanity.com/paper/2410.09487/) (2024)
- [Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2112.07459/) (2021)
- [POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification](https://mlsanity.com/paper/2212.06735/) (2022)
- [An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents](https://mlsanity.com/paper/2110.09076/) (2021)
- [An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters](https://mlsanity.com/paper/2303.12797/) (2023)
- [TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection](https://mlsanity.com/paper/2311.18061/) (2023)
- [Efficient Activation Function Optimization through Surrogate Modeling](https://mlsanity.com/paper/2301.05785/) (2023)
- [Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach](https://mlsanity.com/paper/2406.05088/) (2024)
- [Vertical Power Flow Forecast with LSTMs Using Regular Training Update Strategies](https://mlsanity.com/paper/2009.12167/) (2020)
- [Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting](https://mlsanity.com/paper/2302.10347/) (2023)

## Abstract (arXiv)

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.