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Earlier quoted context omitted.
Do you really think the profitable algo traders are going to tell you that :-)
Why not? Sharing information moves the field forward.
Probably just my own bias because it seems everything I deal with is at least MArP and anomalies are important to my use case.
I can see where this is useful for others, even Amazon suggests ARIMA or ETS if you don't have hundreds of related streams.
Is this more targeted at people who want more smoothing?
Or am I just missing something?
Earlier quoted context omitted.
Do you really think the profitable algo traders are going to tell you that :-)
Why not? Sharing information moves the field forward.
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.
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…
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.
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…
Also a followup question. With timeGPT and chronos advertised as "foundational time series models", do you think they have any value?
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.
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…
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.