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Chronos: Learning the Language of Time Series

arxiv.org

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Re: Chronos: Learning the Language of Time Series

#51

Post by andoando »

BrokrnAlgorithm wrote: post #45

Coming from finance, I always wonder how and if these large pre-trained models are usable on any financial time series. I see the appeal of pre-trained models in areas where there is clearly a stationary pattern, even if its very hidden (i.e industrial or biological metrics). But given the inherently high signal/noise ratio and how extremely non-stationary or chaotic the financial data processes tend to be, i struggl…

Stock prices change continuously based on the current price and future events that have not happened. I don't think they are at all predictable.

Re: Chronos: Learning the Language of Time Series

#52

Post by Galanwe »

gkbrk wrote: post #44
amelius wrote: post #40

Earlier quoted context omitted.

Why not? Sharing information moves the field forward.

You make money with if you have useful data others don't have, or you have better algorithms that others aren't using. When these become publicly known and used, your system doesn't work any more because the prices now include whatever signal you had for yourself before.

It's a bit more subtle than that, because there are feedback loops in the system. When a signal or factor spreads, it does so at multiple time horizons.

e.g. If I have a good signal at predicting horizon 1 day, then it is in my interest to have many people trading it at horizon > 1 day, as they will push the price in my direction.

Re: Chronos: Learning the Language of Time Series

#54

Post by amai »

I doubt the differences in performance between all the „neural“ models are statistically significant. It strikes me as odd that a model like TFT can be the worst of the „neural“ models in one benchmark and at the same time be the best in another benchmark. Also what is the point of Benchmark I ? „It comprises 15 datasets that were also part of the training data of Chronos models“ . That is not forecasting. That is just remembering/overfitting these time series.

Re: Chronos: Learning the Language of Time Series

#55

Post by 555watch »

meow_cat wrote: post #20

Maybe I'm missing something obvious, but what is the idea behind quantizing and tokenizing time series? We tokenize text because text isn't numbers. In the case of time series, we're... turning numbers into less precise numbers? The benefit of scaling and centering is trivial and i guess all timeseries ML does it, but I don't see why we need a token after that.

My primitive understanding is that we approximate a Markovian approach and indirectly model the transition probabilities just by working through tokens.

Re: Chronos: Learning the Language of Time Series

#56

Post by hackerlight »

mikkom wrote: post #34
hackerlight wrote: post #24

Earlier quoted context omitted.

> ensemble of transformer models Isn't that just dropout?

No. Why do you think so?

Geoffrey Hinton describes dropout that way. It's like you're training different nets each time dropout changes.

Re: Chronos: Learning the Language of Time Series

#57

Post by melondonkey »

bethekind wrote: post #47
melondonkey wrote: post #32

As a practitioner the most impactful library for time series has been brms, which basically gives you syntactic sugar for creating statistical models in Stan. Checks all the boxes including probabilistic forecasts, multiple link functions for the likelihood including weiner, gamma, Gaussian, student t, binomial, zero-inflated and hurdle models. Also has auto-regressive and ordinal predictors and you actually learn so…

Could you expand on what you mean by "practitioner?" Also a followup question. With timeGPT and chronos advertised as "foundational time series models", do you think they have any value?

I guess I just mean I’m a data scientist—someone who uses models like these in practice as opposed to someone who develops them.

I’m not sure what to even make of a term like “foundational time series”. Does that just mean it’s widely used and known? You have to earn a role like that you can’t just declare yourself one.

Re: Chronos: Learning the Language of Time Series

#59

Post by ironSkillet »

andoando wrote: post #51
BrokrnAlgorithm wrote: post #45

Coming from finance, I always wonder how and if these large pre-trained models are usable on any financial time series. I see the appeal of pre-trained models in areas where there is clearly a stationary pattern, even if its very hidden (i.e industrial or biological metrics). But given the inherently high signal/noise ratio and how extremely non-stationary or chaotic the financial data processes tend to be, i struggl…

Stock prices change continuously based on the current price and future events that have not happened. I don't think they are at all predictable.

Let me introduce you to https://en.wikipedia.org/wiki/Renaissance_Technologies
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