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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

#101

Post by icapybara »

Xcelerate wrote: post #98
wuj wrote: post #53

Earlier quoted context omitted.

Time series data are inherently context sensitive, unlike natural languages which follow predictable grammar patterns. The patterns in time series data vary based on context. For example, flight data often show seasonal trends, while electric signals depend on the type of sensor used. There's also data that appear random, like stock data, though firms like Rentech manage to consistently find unlerlying alphas. Traini…

Is Rentech the only group that genuinely manages to predict stock price? Seems like the very observation that it’s still possible would be enough motivation for other groups to catch up over such a long period. Also, the first realistic approximation of Solomonoff induction we achieve is going to be interesting because it will destroy the stock market.

Agreed, if stock prices were predictable by some technical means, they would be quickly driven to unpredictability by people trading on those technical indicators.

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

#102

Post by chaos_emergent »

kqr wrote: post #89
bigger_cheese wrote: post #84

Earlier quoted context omitted.

There is potential for integrating ML with time series data in industrial applications (things like smelters, reactors etc.), where you have continuous stream of time series measurements from things like gauges and thermocouples. If you can detect (and respond) to changing circumstances faster then a humans in control room reacting to trends or alarms then potential big efficiency gains... Operator guidance is often…

Every time i've actually tried something like this it has not outperformed statistical process control. It's not just that control charts are great signal detectors, but also managing processes like that takes a certain statistical literacy one gets from applying SPC faithfully for a while, and does not get from tossing ML onto it and crossing fingers.

> Every time i've actually tried something like this it has not outperformed statistical process control.

There are clear counterexamples to your experience, most notably in maintaining plasma stability in tokamak reactors: https://www.nature.com/articles/s41586-021-04301-9

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

#103

Post by whimsicalism »

chaos_emergent wrote: post #102
kqr wrote: post #89

Earlier quoted context omitted.

Every time i've actually tried something like this it has not outperformed statistical process control. It's not just that control charts are great signal detectors, but also managing processes like that takes a certain statistical literacy one gets from applying SPC faithfully for a while, and does not get from tossing ML onto it and crossing fingers.

> Every time i've actually tried something like this it has not outperformed statistical process control. There are clear counterexamples to your experience, most notably in maintaining plasma stability in tokamak reactors: https://www.nature.com/articles/s41586-021-04301-9

task specific model

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

#104

Post by whimsicalism »

cma wrote: post #94
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.

Watch this talk from Albert Gu: Efficiently Modeling Long Sequences with Structured State Spaces https://www.youtube.com/watch?v=luCBXCErkCs They made one of the best time series models and it later became one of the best language models too (Mamba).

I have already watched that talk and know Albert Gu. His work is not about a “foundational” time series model but rather a task specific one.

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

#105

Post by frankc »

icapybara wrote: post #101
Xcelerate wrote: post #98

Earlier quoted context omitted.

Is Rentech the only group that genuinely manages to predict stock price? Seems like the very observation that it’s still possible would be enough motivation for other groups to catch up over such a long period. Also, the first realistic approximation of Solomonoff induction we achieve is going to be interesting because it will destroy the stock market.

Agreed, if stock prices were predictable by some technical means, they would be quickly driven to unpredictability by people trading on those technical indicators.

This is that old finance chestnut. Two finance professors are walking down the hall and one of them spots a twenty dollar bill. He goes to pick it up but the other professor stops him and says "no don't bother. If there was twenty dollars there someone would have already picked it up"

Yes, people arbitrage away these anomalies, and make billions doing it.

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

#106

Post by belter »

Xcelerate wrote: post #98
wuj wrote: post #53

Earlier quoted context omitted.

Time series data are inherently context sensitive, unlike natural languages which follow predictable grammar patterns. The patterns in time series data vary based on context. For example, flight data often show seasonal trends, while electric signals depend on the type of sensor used. There's also data that appear random, like stock data, though firms like Rentech manage to consistently find unlerlying alphas. Traini…

Is Rentech the only group that genuinely manages to predict stock price? Seems like the very observation that it’s still possible would be enough motivation for other groups to catch up over such a long period. Also, the first realistic approximation of Solomonoff induction we achieve is going to be interesting because it will destroy the stock market.

Rentech does not seem to be able to predict the stock market for their customers...

"Jim Simons' Renaissance Technologies suffers $11 billion of client withdrawals in 7 months" - https://markets.businessinsider.com/news/stocks/jim-simons-r...

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

#107

Post by amelius »

Xcelerate wrote: post #98
wuj wrote: post #53

Earlier quoted context omitted.

Time series data are inherently context sensitive, unlike natural languages which follow predictable grammar patterns. The patterns in time series data vary based on context. For example, flight data often show seasonal trends, while electric signals depend on the type of sensor used. There's also data that appear random, like stock data, though firms like Rentech manage to consistently find unlerlying alphas. Traini…

Is Rentech the only group that genuinely manages to predict stock price? Seems like the very observation that it’s still possible would be enough motivation for other groups to catch up over such a long period. Also, the first realistic approximation of Solomonoff induction we achieve is going to be interesting because it will destroy the stock market.

Maybe that would be a good thing. I wouldn't mourn the destruction of the stock market as it's just a giant wealth-gap increasing casino. Trading has nothing to do with underlying value.

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

#109

Post by david_shi »

chaos_emergent wrote: post #60

"Why would you even try to predict the weather if you know it's going to be wrong?" - most OCs on this thread

I have a few qualms with this app: 1. For a Linux user, you can already build such a system yourself quite trivially by getting an FTP account, mounting it locally with curlftpfs, and then using SVN or CVS on the mounted filesystem. From Windows or Mac, this FTP account could be accessed through built-in software.

2. It doesn't actually replace a USB drive. Most people I know e-mail files to themselves or host them somewhere online to be able to perform presentations, but they still carry a USB drive in case there are connectivity problems. This does not solve the connectivity issue.

3. It does not seem very "viral" or income-generating. I know this is premature at this point, but without charging users for the service, is it reasonable to expect to make money off of this?

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

#110

Post by kqr »

chaos_emergent wrote: post #102
kqr wrote: post #89

Earlier quoted context omitted.

Every time i've actually tried something like this it has not outperformed statistical process control. It's not just that control charts are great signal detectors, but also managing processes like that takes a certain statistical literacy one gets from applying SPC faithfully for a while, and does not get from tossing ML onto it and crossing fingers.

> Every time i've actually tried something like this it has not outperformed statistical process control. There are clear counterexamples to your experience, most notably in maintaining plasma stability in tokamak reactors: https://www.nature.com/articles/s41586-021-04301-9

Interesting. Could you point me to where it is compared against SPC? I didn't find it from a cursory read.
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