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

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

#61

Post by neuzhou »

d--b wrote: post #43
nico wrote: post #22

Earlier quoted context omitted.

Is it an issue because it works, or because it doesn’t? Or because it’s bitcoin? I genuinely want to know. Thank you

It is an issue because bitcoin is highly unpredictable. These tools are good at predicting timeseries that are in fact quite predictable. Like insurances will use this to estimate the number of people who will die from cancer in the next year, the year after that, and so on up to 50 years in the future. The model will extrapolate the progresses made in cancer treatment from the current trend, etc. It is a prediction,…

[flagged]

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

#62

Post by konschubert »

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.

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

#63

Post by jcelerier »

teruakohatu wrote: post #6
EmilStenstrom wrote: post #4

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…

What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).

> They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).

well...

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

#64

Post by etrautmann »

konschubert wrote: post #62

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.

The paper suggests it’s for forecasting. How this doesn’t just represent the relatively small number of training samples isn’t obvious to me. If most of the time series for training go up and to the right then I assume that’s what the model will (generally) do, but who knows.

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

#65

Post by JackeJR »

EmilStenstrom wrote: post #4

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…

Actually it can. See https://youtu.be/FUQwijSDzg8?si=LWd5gVNYRd3HH9rJ

Or just search for the James-Stein paradox.

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

#66

Post by dist-epoch »

lordgrenville wrote: post #48
teruakohatu wrote: post #6

Earlier quoted context omitted.

What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).

That's what traditional time-series modelling does. This is a foundational model, which means it's just a neural network trained on lots of time series. (So maybe OP's question still stands? But it's the same question as "how can LLMs be good at so many different kinds of conversations?")

Because traditional time-series modelling (ARIMA, GARCH, ...) is too "simple" and "strict". Just like "simple" computer vision (OpenCV, edge-detection, ...) was crushed by neural networks when having to deal with real world images.

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

#67

Post by [deleted] »

graemep wrote: post #50
teruakohatu wrote: post #6

Earlier quoted context omitted.

What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).

Do these models predict on just a single time series then? it is far more useful for predictions to look for correlations between time series. This is far more complex than looking for correlations in general because most time series trend up or down and therefore correlate.

[deleted]

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

#69

Post by GTP »

FartyMcFarter wrote: post #56
jdthedisciple wrote: post #20

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.

> Shannon would tell us that time forecasting is bullshit If you're trying to forecast random data, then yes, it's bullshit. Otherwise you have a chance.

But, if you don't have the information required for a forecast, then the outcome can look random. We know the physics needed to predict the outcome of a dice throw, but, since to predict the outcome you would need a lot of information that you don't have, the output is random to you.

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

#70

Post by ghywertelling »

teruakohatu wrote: post #41
perks_12 wrote: post #37

Earlier quoted context omitted.

I am not familiar with time series models, but judging from your answer, it would be necessary to feed long time series into this model for it to detect trends. What is a token here? Can it, for the lack of a better example, take in all intraday movements of a stock for a day, a week, a month, etc?

I tend to avoid time series forecasting when I can help it because I find it hard to communicate to stakeholders that a neural network (or another method) is not an oracle. If you are talking about granularity of observations, it would depend on what you are trying to predict (the price in an hour or the price in 12 months?) and how quickly you need the prediction (100ms? Tomorrow morning?). If I had infinite data I…

You can use lightgbm with appropriate feature engineering.
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