Comments inline... On Sat, Apr 26, 2014 at 6:30 AM, Nicholas Mitri <[email protected]> wrote:
> Hello all, > > I'm a little confused by how Input/prediction pairs are formed and plotted > in some of the tutorials. I've attached a 1 step prediction plot for the > sine wave tutorial as well as 1 step and 5 step prediction plots for an > apnea predictor I'm working on for reference. > My understanding is that the number of steps specified for multi step > prediction dictates the delay used to associate an input with the proper > prediction. So, following the sine wave tutorial, part of the code includes > a shifter class that (I assume) queues the predictions until they're > needed. > I'm not exactly sure how the shifter works, but you're probably right. > Looking at the sine plot attached, I don't understand the shifted plots > i.e. the lag in the predicted plot. > While the prediction is usually lagging, there are times when it is not, which means the shifting is occurring properly. The results don't look that usual to me, because you're only a few hundred rows into the data. It takes quite awhile for NuPIC to get better at predicting sines (it's not very good at it anyway). > Also, the anomaly score seems erratic. It's 1 between samples 60 and 100 > when the 2 plots are identical, which is weird in itself since the model > hasn't had enough time to learn the bottom portion of the sine wave. > This doesn't seem unusual to me either. During the time when the score is a flat 1.0, it is continuously wrong with its prediction, simply predicting the last value it saw (that's what is usually does when it doesn't understand any patterns). So an anomaly score of 1.0 during this period makes sense because every value it sees next is misunderstood. > > The same applies to the other attached plots where anomaly is max in > segments where prediction and input are in line, low in segments where they > are off, and 0 for very low input values which is misleading. > > I'd appreciate some guidance in analyzing these plots :). > You know who knows a lot about anomaly scores? ;) Subutai Ahmad. Luckily, he'll be giving a presentation about them [1] at the hackathon next weekend, and I'll be streaming it live on UStream. [1] http://numenta.org/hack/schedule/#anomaly_detection_using_the_cortical_learning_algorithm_subutai_ahmad --------- Matt Taylor OS Community Flag-Bearer Numenta
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