What is not generally understood is that these models don’t predict egg prices or inflation in Italy.

They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling.

They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).

My understanding is that the synthetic training data helps capture abstract time-series patterns that are common in all domains.

As they say in appendix 8:

> We create the synthetic data to reflect common time-series patterns using traditional statistical models. We start with four simple times series patterns:

> • Piece-wise linear trends (I), where the number of the piece-wise linear components is randomly chosen between 2 and 8.

> • ARMA(p, q) (II), where 1 ≤ p, q ≤ 8 and the corresponding coefficients are generated from either a multivariate Gaussian or a uniform, then normalized.

> • Seasonal patterns. In particular we create the sine (III) and the cosine (IV) waves of different random periods between 4 and max context length / 2 time-points and time delays.

If there were no such underlying patterns in the class of all time-series data, then even the idea of traditional time-series models would be fundamentally misplaced.

And since this is a transformer model, it also looks for patterns in the problem-specific input data at inference time, just like how the input context to an LLM influences its output's relevance.

  > How can the same model predict egg prices in Italy, and global inflation in a reliable way?

So, predict sign (branch predictors in modern CPUs also use neural networks of sorts), exponent (most probably it changes slowly) and then predict mantissa using Benford's law.

Actually it can. See https://youtu.be/FUQwijSDzg8?si=LWd5gVNYRd3HH9rJ

Or just search for the James-Stein paradox.

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 actually anything more than curve fitting with better marketing?

I would say:

- decomposition: discover a more general form of Fourrier transform to untangle the underlying factors

- memorization: some patterns are recurrent in many domains such as power low

- multitask: exploit cross-domain connections such as weather vs electricity

> How can the same model predict egg prices in Italy, and global inflation in a reliable way?

How can the same lossy compression algorithm (eg JPG) compress pictures of everything in a reliable way?

Not directly 2024, there was a big update end 2025

I think you meant to link this page: https://research.google/blog/a-decoder-only-foundation-model...

Wish they gave some numbers for total GPU hours to train this model, seems comparatively tiny when compared to LLMs so interested to know how close this is to something trainable by your average hobbyist/university/small lab

That takes me to the same content as the submission, a GitHub repo (Chrome on iOS)

The paper suggests it’s for forecasting. How this doesn’t just represent the relatively small number of training samples isn’t obvious to me. If most of the time series for training go up and to the right then I assume that’s what the model will (generally) do, but who knows.

And https://github.com/amazon-science/chronos-forecasting

If it works for predicting the next token in a very long stream of tokens, why not. The question is what architecture and training regimen it needs to generalize.

There are some other transformer based models on the GIFT leaderboard: https://huggingface.co/spaces/Salesforce/GIFT-Eval

https://www.opentslm.com/

https://www.datadoghq.com/blog/datadog-time-series-foundatio...

https://moment-timeseries-foundation-model.github.io/

https://arxiv.org/abs/2403.07815

A friend at work used one to predict when our CEO would post in Slack, which is verry entertaining to see if correct.

there is TabPFN [1] which also has time series capabilities.

[1] https://priorlabs.ai/tabpfn

we did some internal tests. The quality isn't bad, it works quite well. But it's essentially on the same level of an ARIMA model trained on the data just much bigger and slower.

So in my opinion it currently falls into a kind of void. If your use case is worth predicting and you put a data scientist on it, you're better off just training cheaper ARIMA models.

The safe bet is no. Based on other comments, this would depend a lot on the specific trends you're trying to predict. But it wouldn't work for everything in the stock market.

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 to be knowable is a bit dismissive, no?

Yeah all weather forecasts are just magic

> Shannon would tell us that time forecasting is bullshit

If you're trying to forecast random data, then yes, it's bullshit. Otherwise you have a chance.