TempusBench: Revolutionizing Time-Series Evaluation

Foundation models have done wonders for fields like natural language processing and computer vision. Now, the budding territory of time-series foundation models (TSFMs) is trying to replicate that magic, setting its sights on forecasting. But here's the kicker: they lack a solid framework for evaluation. Enter TempusBench, a new kid on the block aiming to bring order to the chaos.

Why TempusBench Matters

Think of TempusBench as the referee in a game with no rules. The current model evaluation scene is a free-for-all. Many existing benchmarks are based on dated datasets like the M3, which might as well be fossils in the fast-evolving AI world. They're not just old. they also lack clear metadata, making them more myth than science. TempusBench aims to tackle this by introducing new datasets that haven't been seen in TSFM pretraining. Finally, some fresh blood.

But that's not all. The benchmarks we're using right now pretty much ignore key statistical properties like non-stationarity and seasonality. TempusBench isn't having any of that. It offers a set of novel benchmark tasks that dive deeper than your average evaluation.

Leveling the Playing Field

One glaring issue is the unfair comparison between domain-specific models like XGBoost and the broader TSFMs. Why? Because there’s no systematic hyperparameter tuning across models. Imagine comparing apples and oranges. TempusBench brings in a standardized hyperparameter tuning protocol. Finally, a fair fight.

But wait, there’s more. The framework also includes a tensorboard-based visualization tool, giving us a new way to interpret performance. Because data without context is just noise.

The Bigger Picture

So, why should you care? Well, if we can’t evaluate these models fairly, how can we trust their predictions? TempusBench could be the start of a new era for TSFM evaluation. It’s open-source and accessible on GitHub, with a live leaderboard to keep everyone honest. The game comes first. The economy comes second. TSFMs have the potential to change the way we forecast everything from the weather to stock markets, but if we can’t evaluate them properly, they’re just spinning their wheels.

TempusBench represents a much-needed reset in the way we look at time-series data. The question is, will the rest of the field follow suit or stick to outdated methods?