Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting
arXiv:2512.22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications. However, the large quantity of newly proposed architectures and the often contradictory empirical results make it difficult to assess which design choice and model component drives performance. In this position paper, we argue that current benchmarking practices fail to identify the factors responsible for performance differences, thus slowing down progress in the field. In particular, differences in crucial design dimensions are overlooked when comparing architectures, ulti
The proliferation of deep learning models in time series forecasting necessitates a critical assessment of current evaluation methodologies due to growing complexities and often contradictory results.
This paper highlights a foundational issue in AI development, indicating that current benchmarking practices may be misdirecting research efforts and hindering true progress by failing to identify effective design choices.
The focus shifts from simply proposing new architectures to rigorously evaluating how fundamental design choices contribute to performance, potentially leading to more deliberate and effective research directions.
- · AI researchers focused on foundational design
- · Organizations relying on accurate time series forecasts
- · AI ethics and transparency initiatives
- · Researchers focused on 'architecture-of-the-week'
- · Benchmarking platforms lacking design-level analysis
- · AI development cycles prioritizing quantity over quality of models
The call for improved benchmarking standards will likely lead to new methodologies for evaluating deep learning models in time series forecasting.
Better evaluation methods could accelerate the development of more robust, interpretable, and generalizable deep learning models, impacting diverse applications from finance to climate modeling.
A more mature and principled approach to AI development, driven by rigorous benchmarking, could foster greater trust and adoption of AI systems in critical domains, reducing the risk of 'AI winters' or disillusionment.
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Read at arXiv cs.LG