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TimesFM: Time Series Foundation Model for time-series forecasting

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Re: TimesFM: Time Series Foundation Model for time-series forecasting

#41

Post by SpaceManNabs »

Pseudocrat wrote: post #15
VHRanger wrote: post #14

Earlier quoted context omitted.

I imagine they're both worse than good old exponential smoothing or SARIMAX.

Depends on use case. Hybrid approaches have been dominating the M-Competitions, but there are generally small percentage differences in variance of statistical models vs machine learning models. And exponentially higher cost for ML models.

is there a ranking of the methods that actually work on benchmark datasets? Hybrid, "ML" or old stats? I remember eamonnkeogh doing this on r/ML a few years ago.

Re: TimesFM: Time Series Foundation Model for time-series forecasting

#42

Post by baq »

whimsicalism wrote: post #17

I'm curious why we seem convinced that this is a task that is possible or something worthy of investigation. I've worked on language models since 2018, even then it was obvious why language was a useful and transferable task. I do not at all feel the same way about general univariate time series that could have any underlying process.

well... if you look at a language in a certain way, it is just a way to put bits in a certain order. if you forget about the 'language' part, it kinda makes sense to try because why shouldn't it work?

Re: TimesFM: Time Series Foundation Model for time-series forecasting

#45

Post by IshKebab »

whimsicalism wrote: post #17

I'm curious why we seem convinced that this is a task that is possible or something worthy of investigation. I've worked on language models since 2018, even then it was obvious why language was a useful and transferable task. I do not at all feel the same way about general univariate time series that could have any underlying process.

Why not? There are plenty of time series that have underlying patterns which means you can do better than a total guess even without any knowledge of what you are predicting.

Think about something like traffic patterns. You probably won't predict higher traffic on game days, but predicting rush hour is going to be pretty trivial.

Re: TimesFM: Time Series Foundation Model for time-series forecasting

#48

Post by toasted-subs »

wuj wrote: post #46

On a related note, Amazon also had a model for time series forecasting called Chronos. https://github.com/amazon-science/chronos-forecasting

Something I've had issues with time series has been having to use relatively custom models.

It's difficult to use off the shelf tools when starting with math models.

Re: TimesFM: Time Series Foundation Model for time-series forecasting

#50

Post by smokel »

whimsicalism wrote: post #17

I'm curious why we seem convinced that this is a task that is possible or something worthy of investigation. I've worked on language models since 2018, even then it was obvious why language was a useful and transferable task. I do not at all feel the same way about general univariate time series that could have any underlying process.

The things that we are typically interested in have very clear patterns. In a way, if we find that there are no patterns, we don't even try to do any forecasting.

"The Unreasonable Effectiveness of Mathematics in the Natural Sciences" [1] hints that there might be some value here.

[1] https://en.m.wikipedia.org/wiki/The_Unreasonable_Effectivene...

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