My field is primarily audio/vision ML so I don't have any particular domain expertise, but I don't get how you can lump all "time-series" together in one big bucket. My naive understanding is that the way to improve the accuracy is to gain as much joint information as possible. Like:

> Known-future covariates. Some drivers of sensor behaviour are known in advance, such as production schedules, planned shutdowns, weather forecasts. Timestral can condition on them to improve forecast quality, so a scheduled load increase is expected rather than flagged as a surprise.

To me this should be the entire basis of the system, not a condition to improve forecast quality? Like if you're modelling electricity consumption, if all you have is a window of N previous samples, you're going to get completely beaten by a comparatively simple system that knows the time of day, that there's holidays coming up, the weather forecast, etc.

I would also think that these time-series forecasting models would gain from a sophisticated understanding at least the labels of the covariates? A 5M model has no idea what a "bank holiday" is, it just sees an arbitrary class go from 0 to 1, and won't have any useful inductive biases as to what it means or how to link them to other pieces of information.

E.g., wouldn't you predict €/MWh of electricity far more accurately with access to price of natural gas (itself influenced by many geopolitical factors), breakdown in composition of production of each energy source, predictions about roll-out (requiring a sophisticated LLM capable of understanding news and their implications), etc. What's the use in trying to predict it from 2 variables and a narrow window looking back at just 8192 or 32768 samples?

Or perhaps I misunderstand the use-case and this is more for extremely short term movements?

A lot of misconception around the utility of foundational time series models on HN.

Sure, you could train a XGboost model on a time series with holiday or event covariates, but what if you have 10k different series to forecast for? You could train 10k different models, one for each series, but then they have very little history to learn from. Alternatively, you could train a single generic model to cover all 10k, but then you have to deal with scale mismatch between series in addition to the feature dev overhead between series. A TSFM saves a ton of work in modeling each of these series and could potentially generalize better to produce more accurate forecasts.

Any 'vibe check' on these; I don't have benchmarks but I tried out Le Chonk for general coding work that they claim it works really well at and I was very disappointed, it seemed to make all kinds of obvious errors of the kind that I haven't seen from opus, sol, deepseek flash etc in over a year

I like that it’s something different but I’m struggling to think of a way to use this.

It's quite impressive that they get performance so close to the leader t0-gamma but unfortunate about the restrictive license

Very nice. It’s mind-blowing to me that it used to take me months to build and deploy a forecasting model five years ago. Now in minutes I can use something like this, implemented with help from an LLM, and get apparently quite good results (and fine-tune on my data, it seems).

Speed looks nice too. I want an open weight model with this performance now.

My question: What kind of dynamical system observability does Timestral provide? Is it a blackbox y = F(x, params) model for offline use? Can it provide the actual dynamical system equations? Or are you stuck forever querying the Timestral server (which is bad, think about if you're grid system operator doing day ahead, month ahead forecasts, and Timestral goes down)

Typically, in linear, non-linear, and NN models, we fit some noisy data to A) An underlying model with unknown parameters or B) A model free search which attempts to construct combination of "basis" functions or C) A purely black box model which we only care about Input/Output error.

A) is useful because often we know an underlying model we want to match, but don't know the parameter values. If we can determine the parameters, we get insights about the real system. E.g, how are these two variables coupled and how strongly -> We can make business decisions about what happens if we adjust some parameters.

So what exactly does this timeseries foundation model do and provide?

Maybe, the observability is that you have a forecast, and you adjust the covariates and see what happens. But one would want much more insights than a "executive summary" of the forecast.

The important parts for me as a signals processing R&D engineer.

> Classical models such as ARIMA and linear regression remain well-established tools for forecasting time series. These models are usually fitted to a single series and learn only from that series' history.

> Deep learning models such as PatchTST add the capacity to learn nonlinear patterns across many related series, but they are still typically trained from scratch on each dataset, so what they learn doesn't carry over to new domains.

> Time series foundation models bring the pre-training approach to time series. Trained on a large and diverse corpus, our model learns patterns that transfer across series, forecasting zero-shot on data it has never seen, even in domains where data is scarce.

Edit: Looks like it's a pure black box which you can run on premise.

> Output: Probabilistic forecasts, 21 quantiles

> Inputs: Multivariate targets, past-only and known-future covariates

To meaningfully test a forecasting model, use a data-generating process with a high signal-to-noise ratio where a classical method (empirical conditional distributions, i.e., counting) can capture most of the signal. Then check whether the model captures that signal too. If you only test on high-noise processes, any model looks comparable to SOTA because there's little signal to differentiate them. Also testing on a range of signals with different signal-to-noise ratio would allow us to know how much of the signal the model captures in different scenarios.

crazy how they make an improvement of 0.03 look like a 5x difference

perhaps this is wrong, but to me at some abstract level LLMs feel like a more general class of time-series models. like a general purpose sequence model

https://www.nixtla.io/docs/introduction/introduction I remember using that and was quite good so happy that there is more open competition now in the space

Head of Macro here -- this is a great time series forecasting model, we are migrating to this in our pod!

How does it compare to nixtla TimeGPT?

https://github.com/Nixtla/nixtla

I dont know how one can objectively evaluate forecasting. We observe just one universe. We overfit our models to this one observation we have.

Which is why at the end of the day why nobody trusts or blames the forecasting models, specially in all or nothing types of decisions.

Meh....time series data is fundamentally different from textual data. I see why a time series "foundation model" seems appealing, especially to the marketing folks at Mistral. Real-world usability is very limited, which is probably the reason why the blog post features an imaginary customer rather than an actual reference.

I’ve been bullish on time series forecasting since 2006 and always thought transformers were perfectly designed to implement this

Spacetimeformer was imo a huge leap forward that got overshadowed by all the language model progress

Excited to see more effort put into time series forecasting

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Focounder of the timecasting company here - this is some great european sovereignty right there!

Mistral is an embarrassment and clear proof that Europe needs to review its mechanisms for growing and retaining tech talent.

With such strong human capital, both in quantity and quality, Europe should in theory be competitive with US and China in AI.