Well. Lots of math that boils down to 'predicting the future is hard'. Especially when the future is one of social construction, that's what gets lost a bit here. Predicting the future is easier for planetary motions than for Bitcoin.
c7b
I see that a lot of these are markets.
Yes, it’s hard to predict markets. Because anybody who can successfully predict markets, does so, makes money, and changes the market so their predictions lose their edge.
Time series forecasts are a lot easier if you are forecasting, say, disk use in your servers or whatnot. (By “easy” I mean you can do a simple prediction and get useful insights.)
klodolph
A very interesting article, suzyahyah. I especially appreciated your definition of stationarity, a concept with which I struggled in my own time series class. If I understand correctly, it sounds like the basic premise is that a fundamentally statistical methodology (LLMs) can't realistically predict a non-stationary data generation, which makes sense.
Separately, I've wondered for some time if there might be some reliable way to predict non-stationary data. While I don't have the answer, it occurs to me that it will possibly be a non-statistical method due to the fundamental incompatibilities. However, it also occurs to me that, given enough information, every data-generating process actually could be predicted. For instance, in the stock example, if you could model every single input into the system of a single company's stock, including every variable affecting every human that might conduct a transaction of it (daunting and unrealistic as that might be, but this is a thought experiment), then I believe the problem of prediction stops being non-stationary and in fact becomes completely deterministic, if complex. In such a scenario, wouldn't you be able to accurately make your prediction? I believe that perhaps chaos theory could present us with some solutions here where pure statistics (or, rather, simple statistics) cannot.
Just my 2 cents..
EdwardAF-IT
This article focuses mostly on point forecasting. There are many other things that are interesting to forecast.
Consider for example the use case of forecasting the average speed on a road segment with a maximum speed of 70mph. Forecasting whether that will be 69.8 or 70.3 is not very relevant. What is relevant is forecasting when the speed drops below a traffic jam threshold. But the exact timing of that might be impossible to forecast due to the inherently chaotic behavior of traffic. Forecasting the probability of a traffic jam occurring may be more interesting to practitioners.
smokel
I remember in 2017, it was said that "regular" machine learning techniques were better for time series than neural networks. It seems like this blog post is indirectly references that belief and gives. It seems like the idea is simpler ML can be tuned to a given time series whereas neural network require lots of training.
Also, one method works better than another is more meaningful than "predicting the future is hard".
joe_the_user
I was a little surprised to find someone experienced in time series modeling not mentioning bayesian modeling nor the use of ensemble models... but I do think its more fundamental points were worth making.
gregw2
> Given the lack of forecasting signal, the obvious next step then is to seek out external (exogenous) features in the real world that can help prediction models.
this seems right to me. maybe another interesting approach would be a fusion llm+ts model that does multiple-input-single-output with input metadata and causality narrative. so it "thinks" about what data it has and how predictive it may be of the target variable and when something "interesting" occurs it uses the big priors to synthesize a good guess at what it would look like.
so you'd have something like the time series data plus textual narratives of the causality stories as the training data.
a-dub
As touched on in the article: The data's not IID, and there hasn't been very much "time" to generalize across.
verteu
Again and again nerds coming from maths/CS/etc to finance are surprised to find that financial time series are actually impossible to predict. After spending almost a decade in finance now with a similar background, the arrogance of the "let's just throw in some neural network/whatever and be done with it" attitude now amuses me. After all, if it was easy - or even possible with any kind of effort - to predict (even within some error margins or probability) what the stock prices are tomorrow, anyone doing it would quickly become a billionaire or trillionaire. If the person could keep doing it, at some point they would own enough of the stock market so that their actions would affect the prices and whatever pattern they found would vanish.
Financial markets are not a natural phenomenon that exist unchanged regardless of whoever is observing them. Their dynamics continuously change in response to collective actions of all of the humanity.
Oil price changed quite a bit when the US attacked Iran. If you are trying to predict the price of oil, your model would have to be able to predict Trump ordering an attack on Iran. Does your model include a full simulation of the mind of the president of the United States (and every other person who have any kind of impact on the world events)? If not, then your time series forecasts are not going to be that great.
comments (9)
c7b
Yes, it’s hard to predict markets. Because anybody who can successfully predict markets, does so, makes money, and changes the market so their predictions lose their edge.
Time series forecasts are a lot easier if you are forecasting, say, disk use in your servers or whatnot. (By “easy” I mean you can do a simple prediction and get useful insights.)
klodolph
Separately, I've wondered for some time if there might be some reliable way to predict non-stationary data. While I don't have the answer, it occurs to me that it will possibly be a non-statistical method due to the fundamental incompatibilities. However, it also occurs to me that, given enough information, every data-generating process actually could be predicted. For instance, in the stock example, if you could model every single input into the system of a single company's stock, including every variable affecting every human that might conduct a transaction of it (daunting and unrealistic as that might be, but this is a thought experiment), then I believe the problem of prediction stops being non-stationary and in fact becomes completely deterministic, if complex. In such a scenario, wouldn't you be able to accurately make your prediction? I believe that perhaps chaos theory could present us with some solutions here where pure statistics (or, rather, simple statistics) cannot.
Just my 2 cents..
EdwardAF-IT
Consider for example the use case of forecasting the average speed on a road segment with a maximum speed of 70mph. Forecasting whether that will be 69.8 or 70.3 is not very relevant. What is relevant is forecasting when the speed drops below a traffic jam threshold. But the exact timing of that might be impossible to forecast due to the inherently chaotic behavior of traffic. Forecasting the probability of a traffic jam occurring may be more interesting to practitioners.
smokel
Also, one method works better than another is more meaningful than "predicting the future is hard".
joe_the_user
gregw2
this seems right to me. maybe another interesting approach would be a fusion llm+ts model that does multiple-input-single-output with input metadata and causality narrative. so it "thinks" about what data it has and how predictive it may be of the target variable and when something "interesting" occurs it uses the big priors to synthesize a good guess at what it would look like.
so you'd have something like the time series data plus textual narratives of the causality stories as the training data.
a-dub
verteu
Financial markets are not a natural phenomenon that exist unchanged regardless of whoever is observing them. Their dynamics continuously change in response to collective actions of all of the humanity.
Oil price changed quite a bit when the US attacked Iran. If you are trying to predict the price of oil, your model would have to be able to predict Trump ordering an attack on Iran. Does your model include a full simulation of the mind of the president of the United States (and every other person who have any kind of impact on the world events)? If not, then your time series forecasts are not going to be that great.
vjk800