Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
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Swiss finance and banking institutions are increasingly adopting time-series forecasting techniques to predict market trends and manage risk. However, exis
Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
Swiss finance and banking institutions are increasingly adopting time-series forecasting techniques to predict market trends and manage risk. However, existing evaluation protocols for these models have limitations, as they often rely on static train-test splits that can be easily contaminated. To address this issue, researchers have introduced Impermanent, a live benchmark designed to evaluate the temporal generalization of time-series forecasting models. This innovative platform is particularly relevant for Swiss fintech companies and banks seeking to improve their predictive capabilities and mitigate potential biases in model evaluation.
Source
Original Article: Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
Published: March 9, 2026
Author: Azul Garza
This article was automatically aggregated from ArXiv AI Papers for informational purposes. Summary written by AI.
References
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Original Source
This article is based on Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting (ArXiv AI Papers)
