Has anyone gotten this to run on MLX yet?
Google's 200M-parameter time-series foundation model with 16k context
101–110 of 117 posts
Re: Google's 200M-parameter time-series foundation model with 16k context
#102I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way? And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
When I worked on Google Ads, we used time series forecasting to compute the odds of an ad campaign reaching its goal (and to tell users how likely they were to hit them).
A ton of (unsophisticated) advertisers would just draw a line from zero to the number they are at today and project that line to the end of the month to forecast the amount of conversions/spend they were going to hit. This of course doesn't take into account various seasonalities (day-of-week, time-of-year, etc.) and gives you a pretty poor forecast. Compared to those, time-series forecasting is much more accurate.
Is it perfectly accurate? No, that's impossible. But when you can train a model on all advertising campaigns, you can give good 95% confidence intervals.
Re: Google's 200M-parameter time-series foundation model with 16k context
#103Earlier quoted context omitted.
You're crafting features. The modern approach to ML (deep learning) is to use over-parameterized models and let them learn the features. Perhaps you remember this? https://www.nytimes.com/2012/06/26/technology/in-a-big-netwo...
Except that their success in the time series domain has been rather lackluster and elusive. It will s one of the few domains where old school models are not only less work to maintain but also more accurate. There are a few exceptions here and there. Every year there are a few neural nets based challengers. You can follow the M series of computations from its start to see this evolution.
[deleted]
Re: Google's 200M-parameter time-series foundation model with 16k context
#104Earlier quoted context omitted.
That is disappointing. One would say that with all the budget and compute, Google would be able to create something that beats methods from 70s. Maybe we are hitting some hard limits. Maybe it would be better to train an LLM with various tuning methodologies and make a dedicated ARIMA agent. You throw in data, some metadata and requested window of forecast. Out comes parameters for "optimal" conventional model.
I think this could be an interesting read for you, I read it last week and it kind of argues the same points: https://shakoist.substack.com/p/against-time-series-foundati...
thanks for sharing.
i met an associate working for a particular VC and they were really into time series foundational models. I argued the most of the "Why real forecasting problems break the whole frame" as to why they were wasting their time at that time.
she was totally convinced i was wrong because she was discussing investing with some top and well respected researchers that were really pushing this and wanted to make a startup around it.
i was and am still confused as at all the wishful thinking. then again, sometimes the best time to sell an idea is right before you think it is possible.
Re: Google's 200M-parameter time-series foundation model with 16k context
#105Earlier quoted context omitted.
It still doesn't pretty well on text. And we have newer formats and ideas that would also deal with that. (To be really dead simple: have a minimal container format that decides between png or jpg, use png for text.) However: white noise is where it really struggles. But real pictures of the real world don't look like white noise. Even though in some sense white noise is the most common type of picture a priori. Simi…
White noise is random, so it's incompressible by definition. By JPG or by any other method no matter how clever.
What you're saying is only true for lossless compression, if you're fine discarding data you can compress anything. Try it yourself:
magick -size 512x512 xc:gray +noise Random noise.png
magick noise.png -interlace Plane -quality 75 compressed_noise.jpg
Re: Google's 200M-parameter time-series foundation model with 16k context
#106I'm willing to bet an intelligent LLM with a dataset and a pandas stats package could outperform this model by running its own experiments and making predictions
Instead of willing to bet, you can do it yourself and prove it. It is not like there is a ceiling for doing what you are proposing.
I am willing to bet that you are wrong.
Re: Google's 200M-parameter time-series foundation model with 16k context
#107Earlier quoted context omitted.
That's what traditional time-series modelling does. This is a foundational model, which means it's just a neural network trained on lots of time series. (So maybe OP's question still stands? But it's the same question as "how can LLMs be good at so many different kinds of conversations?")
Because traditional time-series modelling (ARIMA, GARCH, ...) is too "simple" and "strict". Just like "simple" computer vision (OpenCV, edge-detection, ...) was crushed by neural networks when having to deal with real world images.
Sometimes you want inductive bias. No universally true claim can be made like this.
Re: Google's 200M-parameter time-series foundation model with 16k context
#108Let me be blunt: Shannon would tell us that time forecasting is bullshit: There is infinitely more entropy in the real world out there than any model can even remotely capture. The world is not minecraft.
Time series forecasting has proven useful in a number of different domains from weather to health monitoring. Sure you can easily over fit on the training data, but in general that's a data source/input problem where you need many high quality data sources to find the signal in the noise. The world is chaotic sure, but there are still truths to be found in noisy time series data; saying that the world is too random t…
I agree when it comes to highly niche applications with a generous SNR.
Universal models though?
And I haven't even mentioned the fact that en mass forecasting ITSELF may influence the subject of forecasting.
Re: Google's 200M-parameter time-series foundation model with 16k context
#109isn't this basically prophet?
No. Prophet is based on curve-fitting.
Re: Google's 200M-parameter time-series foundation model with 16k context
#110I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way? And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
It’s not really predicting “egg prices” or “inflation” — it’s mostly fitting patterns that happen to show up in those series. The problem isn’t domain generalization, it’s that we keep pretending these models have any notion of what the data means. People ask how one model can understand everything, but that assumes there’s any understanding involved at all. At some point you have to ask: how much of “forecasting” is…
I don't know how I feel about LLM slop coming to HN.