I’m now sending data through the system in 10 dimensions and getting predictions for my data. After 10000 timesteps at 1 minute using SDRClassifiers and
AUTO_CLASSIFY, I’m seeing roughly 10% of difference between the values sent and the values predicted for 1 minute ahead of time. And it gets to a good level of prediction relatively quickly
However, I need to make predictions hours or days in advance. So a few questions come to mind:
- (most importantly) How can I set up the Network so that the classifier gets other step sizes beyond the default of 1?
Also, while I’m at it:
- this is called a “classifier” but it seems to be doing what I would call a regression. Is there a way in which it might perform actual classification on data?
- I presume if I want to make predictions of shapes in the data, I should send a series of points in each dimension lagged by the width of the shape I’m looking for?