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.)

It's true that markets are more _adversarial_. But there's still a lot of trouble with distribution shifts even in server metrics. As an example, our SRE team got paged a few times in the past month for traffic drops due to the World Cup. This stresses the nowcasting alert in several dimensions:

- there's no seasonal pattern to the matches, they happen sorta randomly.

- they drive increased query traffic in the hour or so before the game

- then during the game usage drops, sometimes to below "normal" depending on time of day and who's playing

So... now the accuracy of your forecasting tool depends on correctly predicting when world cup matches happen, and also who wins them!

edit: and this is just one recent example. others involve severe weather, national gameshows, earthquakes, and when you celebrate christmas.

Between the difficulty and value propositions for forecasting timeseries "disk usage" versus "financial markets" there are some rather relevant time series such as "company sales" or "demand for company product" that come up again, and again, and again but are neither "easy" nor "predicting-the-stock-market-hard".