Confidence intervals don't work like that. If the data exists then you don't state a confidence interval. You never know the data when you state confidence intervals.
Here's why your error is a poor stat. One, your error makes a time series itself. Condensing it like that and keeping all the historic results in the current error is not useful. Your error tells the user very little about recent applicability. Secondly, you don't make any effort to tell the user how your mean varies. The minimum is zero and the mean is 1.3% okay, but how about the variance? What's the tail look like? That's a very important piece of information that anyone who is more than slightly curious will miss from your site. Listing a mean with no variance or other info is meaningless when the underlying distribution is largely unknown.
I know very little about the method used to generate your model. I know nothing about AI. I'm a mathematician I spend all my time modelling. You shouldn't condense 24 time series (error) into one number with equal weighting like that it isn't a relevant statistic to anyone but you. That means your predictions for the price 2 years ago are as relevant as your ability to predict yesterdays price as far as your error goes.
The vast majority don't understand the error at all. They just don't look at it long enough to understand it or simply don't care or know enough to see why its flawed.
PS there are confidence interval methods for neural network models. Your method for calculation is an innaproriate statistic, no matter how many people 'understand it'.
As someone who knows nothing about neural networks, I don't think you are in any place to say that my method of calculation is an inappropriate statistic (also that statement doesn't make sense in English).
As someone who knows much less about who does and doesn't understand what aspects of my website than I do, I don't think you are in any place to say that the vast majority of people don't understand the error at all (which is false).
As someone who knows very little, if anything, about pattern recognition and function approximation, I don't think you are in any place to say that the error calculated on data from 2 years ago is any less relevant than the error calculated on data from 2 days ago. They are equally relevant and the fact that you disagree with this just continues to confirm your lack of understanding of my method.
The only reasonable point you've made is that I could show the variance distribution. However, I address this on the about page in a very non-mathematical way. I tell people that the prices are less accurate when real-life events are affecting the price, and they are more accurate when prices are stable. Statistics are not needed to back this statement, as it is pretty intuitively obvious and it gives people enough information to make their own intelligent judgement without having to clutter the site with a bunch of statistics that are meaningless to almost everyone (except you and the 3 other mathematicians who are looking at my site).
There are people out there who are skeptical of my predictions and even skeptical of my error figures, and that is perfectly reasonable. However, it is ridiculous when people who have no understanding of what I do criticize my methods. I understand that you have some background in math, but you clearly don't understand how non-mathematicians (i.e. almost every person on Earth) look at things and understand data. You are the only person of 13,000 who has expressed any issue with my method of calculating this number. I could clutter the site with tons of detailed statistics and you might be able to have a thorough understanding of the error, but nobody else would.
My current average error is one number - simple. It describes all of the data at once, and it is not misleading because everyone knows the 1hr prediction is likely to be more accurate than the 24hr prediction.
You can continue to hate on my project for no reason if you want, but you have no point here, I'm sorry...