Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting

TL;DR

Impermanent introduces a live, continuously updated benchmark for evaluating the temporal robustness and generalization of time series forecasting models using real-world, non-stationary data streams from GitHub activity.

Contribution

It presents a novel live benchmarking framework that assesses models over time, addressing limitations of static test sets and enabling ongoing evaluation of temporal generalization.

Findings

Evaluates models on non-stationary GitHub data streams.

Provides a platform for ongoing, reproducible model comparison.

Highlights challenges in maintaining performance over evolving data.

Abstract

Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style models. While these models often claim broad generalization, existing evaluation protocols provide limited evidence. Indeed, most current benchmarks use static train-test splits that can easily lead to contamination as foundation models can inadvertently train on test data or perform model selection using test scores, which can inflate performance. We introduce Impermanent, a live benchmark that evaluates forecasting models under open-world temporal change by scoring forecasts sequentially over time on continuously updated data streams, enabling the study of temporal robustness, distributional shift, and performance stability rather than one-off accuracy on a frozen test set. Impermanent is instantiated on GitHub open-source activity, providing a naturally live and highly non-stationary dataset shaped by releases, shifting contributor behavior, platform/tooling changes, and external events. We focus on the top 400 repositories by star count and construct time series from issues opened, pull requests opened, push events, and new stargazers, evaluated over a rolling window with daily updates, alongside standardized protocols and leaderboards for reproducible, ongoing comparison. By shifting evaluation from static accuracy to sustained performance, Impermanent takes a concrete step toward assessing when and whether foundation-level generalization in time-series forecasting can be meaningfully claimed. Code and a live dashboard are available at https://github.com/TimeCopilot/impermanent and https://impermanent.timecopilot.dev.