The unreasonable difficulty of time series forecasting

> I’ve been thinking recently about what makes time series forecasting problems so difficult compared to other sequence learning tasks

Whenever I teach people time series forecasting, I always point out that one of the biggest challenges is that you will always have values at prediction time that are out side the range of values observed during training (specifically the value of t).

In plenty of other machine learning and statistical modeling tasks this is not the case. You can train on every token you'll ever see and every pixel value you'll ever see, you can do regression analysis on every categorical value you include and an least an observation from within a range of every continuous and discrete value you'll observe. But with forecasting you will always have values you predict that are outside the range of anything you trained on.

You would run into similar problems if you tried to create a statistical model of the density of water given a temperature but your training data only included values between 0-100 C and you went out and started predicting values covering all the temperatures found on Earth.

For whatever reason, when time is a variable we somehow think it is immune from the obvious limitation of predicting on values outside of the range of values you trained on.

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.)

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.

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.

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.

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..

The question "what probability distribution generated this" is very hard to answer if you're only getting short window before the distribution changes. A lot of distributions could generate relatively "short" set dispersed data points and deciding which distribution is "really" creating the points might not even be meaningful. A distribution is a mathematical device, not something with a physical existence.

> 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. After all many real world time series are event-driven (FX, bitcoin) and hence they are exposed to shocks and drifts which can also be measured or accounted for in the data generating process.

This is the approach I'm using at my job, which is incident detection with customer metrics. We're tagging our time series data with common features -- such as country, customer type, etc -- with the idea that we can do a graph-like search to find exogenous variables. We can also use this to identify time series that have a similar "data generating process" and are simply different "realizations" of each other.

We don't need great time series forecasts, just something that detects large deviations quickly. We can then add in an existing dataset of _known_ incidents, indexed by the same common features, as a training/validation set.

> 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.

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.

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".

As touched on in the article: The data's not IID, and there hasn't been very much "time" to generalize across.

I have to explain this to managers, execs and stakeholders all the time. ML models work great for systems where the rules/dynamics do not change over time. With Forecasting, in a lot of domains where you want a forecast, everything is subject to change - laws, policies, regs, customers appetites, competitors behaviours, etc.

Are there any real-world time-series that are known to be easy to forecast? Something I could use as an example of "this is what forecasts look like when it's actually possible to predict your time-series and your model is actually doing it"?

The examples in the posts suggest that the past does not contain all the patterns, or information in general, about the future. If so, isn't it natural that point forecast will fail in some cases?

"It is difficult to make predictions, especially about the future."

Is there a github repo for her time series forecasting experiments? I'd like to see her code and be able to run it against my own data sets.

Knowing this forever. Simple is better, overtraining is overtraining. Maybe I’m biased by American pragmatism or something.

Not at all unreasonable, why should predicting the future be easy for those of us living in the simulation?

Can't help but see hurricane-prediction spaghetti plots in those charts.

Maybe trying to predict entropy would be easier

Well, Jane Street cracked it