A lot of misconception around the utility of foundational time series models on HN.

Sure, you could train a XGboost model on a time series with holiday or event covariates, but what if you have 10k different series to forecast for? You could train 10k different models, one for each series, but then they have very little history to learn from. Alternatively, you could train a single generic model to cover all 10k, but then you have to deal with scale mismatch between series in addition to the feature dev overhead between series. A TSFM saves a ton of work in modeling each of these series and could potentially generalize better to produce more accurate forecasts.

But why is the question, I run about 400k models every 30 minutes at work. I have tried all other approaches, but xgboost model per timeseries has never been beaten before. Even if I wasn't targetting accuracy, Xgboost loading a pretrained model and predicting with it and still faster.

What's the input like? Does it incorporate language understanding, so that each time series has a natural language explanation of the series and all the data fields? If it takes in really descriptive text like that I can see the opportunity for generalization. If it's just a time series of non-descript numbers I see much much less opportunity for cross-series learning.

> 10k different series

who has these though? i cannot imagine somoone making sense of 10k timeseries.