[teruakohatu]

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).

[jcelerier]

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

well...

[morkalork]

Next you'll suggest something looney like a correlation with the 11-year solar cycle!

(for those who are lost: https://x.com/onionweigher/status/1936630237208469898)

[guntars]

The Middle East war season is upon us once again

[Forgeties79]

Born too soon to deploy to the Middle East.

Born too late to deploy to the Middle East.

Born just in time to deploy to the Middle East.

[lordgrenville]

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?")

[dist-epoch]

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.

[robot-wrangler]

This seemed like a good answer at first. But on further thought, images on the whole really do seem to have quite a bit more standard structure / "grammar" to exploit compared to arbitrary time-series. Many images are of the world, where there is gravity so you might see preponderance of blobs at the bottom, or the repetitive types like people, animals, faces, eyes. Wildly abstract images still have some continuity, pixels in a neighborhood are likely to be similar.

Time series in general have none of this kind of structure that's strictly necessary. I'm sure that many real-world sensors typically have some gaussian distribution aspects + noise and/or smoothness and locality types of assumptions that are pretty safe, but presumably that simple stuff is exactly what traditional time-series modelling was exploiting.

Maybe the real question is just what kind of time-series are in the training data, and why do we think whatever implicit structure that is there actually generalizes? I mean, you can see how any training that mixes pictures of dogs and cats with picturing of people could maybe improve drawing hair, detecting hair, or let you draw people AND dogs. It's less clear to me how mixing sensor data / financial data / anything else together could be helpful.

[dist-epoch]

> It's less clear to me how mixing sensor data / financial data / anything else together could be helpful.

Because many of these have the same underlying causal structures - humans doing things, weather correlations, holidays.

Well studied behavioral stuff like "the stock market takes the stairs up and the elevator down" which is not really captured by "traditional" modelling tools.

I'm sure people will be doing mechanical interpretation on these models to extract what they pattern match for prediction.

[torginus]

Personally, coming from an EE background and not finance or statistics, I would go about identifying these patterns with an Signals & Systems toolbox, like systems identification, various matched filters/classifiers.

This might be a totall wrong approach, but I think it might make sense to try to model a matched filter based on previous stock selloff/bullrun trigger events, and then see if the it has any predictive ability, likewise the market reaction seems to be usually some sort of delayed impulse-like activity, with the whales reacting quickly, and then a distribution of less savvy investors following up the signal with various delays.

I'm sure other smarter people have explored this approach much more in depth before me.

[esafak]

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...

[robot-wrangler]

> Because many of these have the same underlying causal structures - humans doing things, weather correlations, holidays.

Or, you know, maybe they aren't. Thermometers and photon counts are related to weather sometimes, but not holidays. Holidays are related to traffic sensors and to markets, but not Geiger counters.

> Well studied behavioral stuff like "the stock market takes the stairs up and the elevator down" which is not really captured by "traditional" modelling tools.

Prices are the opposite, up like a shot during shocks, falling slowly like a feather. So that particular pattern seems like a great example of over-fitting danger and why you wouldn't expect mixing series of different types to be work very well.

[dist-epoch]

Electricity demand is influenced very strongly by holidays, strongly by weather and from weak to strong by geopolitics (depending on location).

The model will have a library of patterns, and will be able to pattern match subtle ones to deduce "this time series has the kind of micro-patterns which appear in strongly weather influenced time-series", and use this to activate the weather pattern cluster.

To use your example, when served thermometer data, the model notices that the holiday pattern cluster doesn't activate/match at all, and will ignore it.

And then it makes sense to train it on the widest possible time series, so it can build a vast library of patterns and find correlations of activation between them.

[energy123]

Sometimes you want inductive bias. No universally true claim can be made like this.

[cybrox]

Wars in the middle east seem to have increasingly regular patterns tied to stock market opening hours, unfortunately.

[rubyn00bie]

I totally agree with the sentiment but from what I can tell, I’d say they tend happen immediately before or after markets open and close. Essentially, and to their maximum, screwing absolutely everyone who isn’t in the clique from participating in the trade.

FWIW— the only sure fire way to win the trade is to buy time and assume both gross incompetence and negligence when it comes action. The only caveat is if the markets tank enough, this administration will signal capitulation before hand, e.g. Trump mildly capitulating on tariffs last April after the markets proceed to relentlessly defecate themselves.

0-DTE options are typically, and for good reason, stupid gambles. But, right now they can’t even be considered gambling, because there’s zero chance of winning. Not just bad odds, but no odds. Again just signaling how truly malicious this admin is and its disdain for anyone and everyone not close to them.

[jofzar]

I mean it's super obvious, it's directly tied to scrubs popularity.

New season of scrubs = new war in the middle east.

[FartyMcFarter]

Wow, I didn't know. Thank you! Such a great show.

[jofzar]

It's suprisingly good, like it's it's 100% worth watching if you liked scrubs.

[perks_12]

I am not familiar with time series models, but judging from your answer, it would be necessary to feed long time series into this model for it to detect trends. What is a token here? Can it, for the lack of a better example, take in all intraday movements of a stock for a day, a week, a month, etc?

[teruakohatu]

I tend to avoid time series forecasting when I can help it because I find it hard to communicate to stakeholders that a neural network (or another method) is not an oracle.

If you are talking about granularity of observations, it would depend on what you are trying to predict (the price in an hour or the price in 12 months?) and how quickly you need the prediction (100ms? Tomorrow morning?). If I had infinite data I would use granularity as a hyper parameter and tune that to a level that produced the best test results.

I am for example currently using weekly averages for non-price data forecasting. I could use daily data but weekly is absolutely adequate for this purpose.

[ghywertelling]

You can use lightgbm with appropriate feature engineering.

[teruakohatu]

Using many different models, just not NN for this particular application.

[amelius]

What makes these models different from models used for e.g. audio?

Or other low-dimensional time domain signals?

[carschno]

You could abstract speech or other audio as a series of sounds, where time is indeed a factor. Speech, however, has patterns that are more similar to written language than to seasonal patterns that are typically assumed in time series. While trained on different data, the architecture of TimesFM is actually similar to LLMs. But not identical, as pointed out at https://research.google/blog/a-decoder-only-foundation-model...:

> Firstly, we need a multilayer perceptron block with residual connections to convert a patch of time-series into a token that can be input to the transformer layers along with positional encodings (PE).

> [...]

> Secondly, at the other end, an output token from the stacked transformer can be used to predict a longer length of subsequent time-points than the input patch length, i.e., the output patch length can be larger than the input patch length.

[amelius]

If "seasonal patterns" is the thing that differentiates between these two data sources, then perhaps time series models should be called seasonal models?

[graemep]

Do these models predict on just a single time series then?

it is far more useful for predictions to look for correlations between time series. This is far more complex than looking for correlations in general because most time series trend up or down and therefore correlate.

[ReptileMan]

It is the Middle East. Wars are always in season. And supply is more than the demand.

[d--b]

The main issue is that people do use them to predict bitcoin prices intraday and that sort of things.

[nico]

Is it an issue because it works, or because it doesn’t? Or because it’s bitcoin?

I genuinely want to know. Thank you

[d--b]

It is an issue because bitcoin is highly unpredictable.

These tools are good at predicting timeseries that are in fact quite predictable. Like insurances will use this to estimate the number of people who will die from cancer in the next year, the year after that, and so on up to 50 years in the future. The model will extrapolate the progresses made in cancer treatment from the current trend, etc. It is a prediction, cause it's still possible that a breakthrough comes in and suddenly people don't die from a certain form of cancer, but generally it should be roughly correct.

Bitcoin prices are a lot more chaotic, influenced by a ton of unrelated events that shape its path a certain way. There is absolutely no certainty that studying the shape of its past evolution will help in any way understand its future evolution.

Of course here I mean by studying its price alone. If you add more information, like who's behind each trend and why, you have a much better sense of what could happen next.

[visarga]

ARIMA and ARMA models

[a-dub]

ar(k) stuff, sure. that's old news. i would expect the newfangled stuff to be good at 0-shot learning of pre-event signatures spread across multiple series, at a minimum.