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Comment by mikehollinger | Hacker News Reader

This doesn’t capture work that’s happened in the last year or so.

For example some former colleagues timeseries foundation model (Granite TS) which was doing pretty well when we were experimenting with it. [1]

An aha moment for me was realizing that the way you can think of anomaly models working is that they’re effectively forecasting the next N steps, and then noticing when the actual measured values are “different enough” from the expected. This is simple to draw on a whiteboard for one signal but when it’s multi variate, pretty neat that it works.

[1] https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1

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My similar recognition was when I read about isolation forests for outlier detection[0]. When predictions are different from the average, something is off.

[0] https://scikit-learn.org/stable/modules/generated/sklearn.en...

what were you thinking then before your aha moment? :D

> what were you thinking then before your aha moment? :D

My naive view was that there was some sort of “normalization” or “pattern matching” that was happening. Like - you can look at a trend line that generally has some shape, and notice when something changes or there’s a discontinuity. That’s a very simplistic view - but - I assumed that stuff was trying to do regressions and notice when something was out of a statistical norm like k-means analysis. Which works, sort of, but is difficult to generalize.

> Like - you can look at a trend line that generally has some shape, and notice when something changes or there’s a discontinuity.

what you describe here is effectively forecasting a model of what is expected to happen and then you notice a deviation from it.

to me its always amazing how people look at whats evidently obvious to me and say its profound.

especially if they are self-assessed "distinguished engineers and master inventors"

Care to share the contexts in which someone needs a zero-shot model for time series? I have just never come across one in which you don't have some historical data to fit a model and go from there.

In this case I don't think zero-shot means no context. I think it's more used in relation to fine-tuning the model parameters over your data.

> TTM-1 currently supports 2 modes:

> Zeroshot forecasting: Directly apply the pre-trained model on your target data to get an initial forecast (with no training).

> Finetuned forecasting: Finetune the pre-trained model with a subset of your target data to further improve the forecast

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