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.
Also, the first realistic approximation of Solomonoff induction we achieve is going to be interesting because it will destroy the stock market.
Yes, people arbitrage away these anomalies, and make billions doing it.
"Jim Simons' Renaissance Technologies suffers $11 billion of client withdrawals in 7 months" - https://markets.businessinsider.com/news/stocks/jim-simons-r...
So you think the multi-trillion dollar stock market, consisting of thousands of global companies, has no use beyond "pulling money out of systems"? Weird.
Just to say, weirdness happens.
There's an extensive body of literature across numerous domains that demonstrates the benefits of Multi-Task Learning (MTL). Actually I have a whole folder of research papers on this topic, here's one of the earliest references on hand that I feel captures the idea succinctly in the context of modern ML:
“MTL improves generalization by leveraging the domain-specific information contained in the training signals of related tasks" [Caruana, 1998]
I see repetition and structure everywhere in life. To me it's not far fetched that a model trained on daily or yearly trends could leverage that information in the context of e.g. biological signals which are influenced by circadian rhythm etc.
Disclaimer: my background is in ML & bio-signals, I work with time series too much.
"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...
But you wouldn't want this model for file upload storage usage which only increases, where you would put alerts based on max values and not patterns/periodic values.
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.
Operator guidance is often based on heuristics - when metric A exceeds X value for Y seconds take action Z. Or rates of change if the signal is changing at a rate of more than x etc.
So in these areas there exists potential for ML solution, especially if it's capable of learning (i.e. last response overshot by X so trim next response appropriately).
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.
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
After all, the physical world (down to the subatomic level) is governed by physical laws. Ilya Sutskever from OpenAI stated that next-token prediction might be enough to learn a world model (see [1]). That would imply that a model learns a "world model" indirectly, which is even more unrealistic than learning the world model directly through pre-training on time-series data.
People on the AI-hype side of things tend to believe this, but I really fundamentally don't.
It's become a philosophical debate at this point (what does it mean to "understand" something, etc.)
There's a huge industry around time series forecasting used for all kinds of things like engineering, finance, climate science, etc. and many of the modern ones incorporate some kind of machine learning because they deal with very high dimensional data. Given the very surprising success of LLMs in non-language fields, it seems reasonable that people would work on this.
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).