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Google's 200M-parameter time-series foundation model with 16k context

276 pointsby codepawltoday at 5:21 AM101 commentsview on HN

Comments

EmilStenstromtoday at 5:45 AM

I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way?

And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…

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kuutoday at 7:39 AM

It would be nice to add (2024) to the title, this is not news (see: https://research.google/blog/a-decoder-only-foundation-model...)

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EmilStenstromtoday at 5:41 AM

Here is the link to the blogpost, that actually describe what this is: https://github.com/google-research/timesfm?tab=readme-ov-fil...

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pplonski86today at 8:40 AM

Can someone explain ELI5 how it does work? and how many data points it can read?

dash2today at 6:27 AM

So the time series are provided with no context? It's just trained on lots of sets of numbers? Then you give it a new set of numbers and it guesses the rest, again with no context?

My guess as to how this would work: the machine will first guess from the data alone if this is one of the categories it has already seen/inferred (share prices, google trend cat searches etc.) Then it'll output a plausible completion for the category.

That doesn't seem as if it will work well for any categories outside the training data. I would rather just use either a simple model (ARIMA or whatever) or a theoretically-informed model. But what do I know.

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mikert89today at 4:11 PM

I'm willing to bet an intelligent LLM with a dataset and a pandas stats package could outperform this model by running its own experiments and making predictions

ratoday at 6:26 AM

This has been around a few months now, has anyone built anything on it?

konschuberttoday at 10:21 AM

Let's say I have long time series of past solar irradiation and long time series of past weather forecasts. Can this model make use of weather forecasts for time X in the future to predict electricity prices in the future?

That is, can it use one time series at time X to predict another time series at time X?

Or is this strictly about finding patterns WITHIN a time series.

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Foobar8568today at 5:36 AM

Somehow I missed that one. Are there any competition on this?

I always had difficulties with ML and time series, I'll need to try that out.

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htrptoday at 1:56 PM

isn't this basically prophet?

aris0today at 4:17 PM

Has anyone gotten this to run on MLX yet?

emsigntoday at 7:20 AM

Can this finally break the stock markets?

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raghavMultilipitoday at 7:10 AM

This has been around a few months now, has anyone built anything on it?

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croemertoday at 8:07 AM

(2024)

jdthediscipletoday at 6:26 AM

Let me be blunt: Shannon would tell us that time forecasting is bullshit:

There is infinitely more entropy in the real world out there than any model can even remotely capture.

The world is not minecraft.

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charlotte12345today at 7:15 AM

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