Recent time series foundation models such as LagLlama, Chronos, Moirai, and TinyTimesMixer promise zero-shot forecasting for arbitrary time series. One central claim of foundation models is their ability to perform zero-shot forecasting, that is, to perform well with no training data. However, performance claims of foundation models are difficult to verify, as public benchmark datasets may have been a part of the training data, and only the already trained weights are available to the user.

Therefore, performance in specific use cases must be verified based on the use case data itself to ensure a reliable assessment of forecasting performance. sktime allows users to easily produce a performance benchmark of any collection of forecasting models, foundation models, simple baselines, or custom methods on their internal use case data.