I have multiple data inputs (let’s say 10 for example) and am doing anomaly detection and prediction of the data in column-2. Column 1 contains the time-stamp, column 2 contains the desired output, and columns 3-12 contain the 10 inputs HTM is analyzing to predict column 2.
I swarm over the data and save the resulting model. I then loop over each row in the data calling model.run() and passing in only two arguments: time-stamp and column 2 (desired-output). Everything seems to work fine, and it magically spits out predictions that are quite accurate. But, I don’t know how to feed in new data from the 10 inputs into the model, which wasn’t available when it swarmed over the data.
Should I pass any new data into model.run() as a part of the input record? Is Model.setFieldStatistics() the right method to use? What’s the best way to accomplish this?