I just used random times. Not news based. You saw the times I posted. I don't think they were news based(?) (+ the prediction you made in this thread). I used to work for a company to make predictions about sales and now I work a master thesis about nearly the same. I think I have some idea about statistics. You know about the normal distribution? 6 predictions 500% over the average estimate. Really? This is nearly unlikely like a lottery ticket.
Ok, another one. 1pm. 1pm it estimates 380$ for tomorrow. The 1.3% range is 375 and 385$. Let's see!
PS: You should use test data. Take your network for 1.4 backwards and test it to predict the price of 2.4. and so on. Then you get real prediction (maybe you should not use the last 2 voltatile days)
Once again, you are asking questions that you would know the answer to if you read my site. You can see where I originally got the data from on the website (and I've been updating it using the bitstamp API since then).
Also making a 3-layer neural network is not rocket science but it's definitely very far from a simple task as well. Even a 2 layer neural network is complicated. Any number of layers above that, though, would be of equal difficulty to create.
Pretty soon I'm going to have charts up showing actual prices vs predicted prices. If you really want to do some measurement yourself though, you obviously can't just pick random times. You have to look at all 24 predictions from multiple 24 hour predictions and compare them to the actual prices, and take the average of those errors. In order to really get a solid measurement of this system's accuracy you would have to do this (as in test 24 predictions) at least like 40 or 50 times (and this is still a very small sample) and it would have to be over the course of at least a few weeks. Honestly, this would give you a tenuous grasp of the system's accuracy at best. 6 "random" predictions gives you absolutely zero indication of its accuracy.
Ok, random #7:
Your said, we get to 375 to 385. Real price was 425$. Error: 10.5%
Counter prediction: "price now will be price in 24hours": Error: 1.2%
If you know statitics and assume a normal distribution, you know how unlikely your real prediction error is only 1.3%. It is rather like 5-7% and worse like the counter prediction. 7 random tests are a lot. 7 times an 500% error in a row.
Again: I don't say 7 random predictions are enough to to measure your accurancy. I just say disprove the average error of 1.3% for real predictions.
Ok, another one:
Current price: 414.3$
Your neural network predicts: 417$
The good thing is, that this time, the counter prediction(same price in 24h) will be not really better than your neural network prediction