[BrokrnAlgorithm]

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 struggle to see the use of pre-trained foundation models.

[andoando]

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.

[ironSkillet]

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

[bethekind]

I played around with timeGPT beta against predicting the sp500 index performance for the next day (not multi variate time series as I couldn't figure out how to get it setup) and trying to use the confidence intervals it generated to buy options was useless at best

I can see chronos working a bit better, as it tries to convert trends, and pieces of time series into tokens, like gpt does for phrases.

Ie. Stock goes down terribly, then dead cat bounces. This is common.

Stock goes up, hits resistance due to existing sell orders, comes down

Stock is on stable upward trend, continues upward trend

If I can verbalize these usual actions, it's likely chronos can also pickup on them.

Once again quality of data trumps all for LLM's, so performance might vary. If you read the paper, they point out a few situations where the LLM is unable to learn a trend, ie. When the prompting time series isn't long enough.

[intalentive]

Imitation learning of discretionary traders who rely on a mixture of rules and intuition.