Hello, I've been working with an energy competition dataset [1] and I've been experimenting with some different ways to predict many steps ahead (I have to predict 31 different energy loads for the whole month). This led me to some questions:
1) Has anyone tried feeding one-step classifier predictions back to the input? This can be done easily by hand but I'm not sure if this is a good idea for many steps prediction. 2) Does "disableLearning" also turn off classifier learning? If not, how do I do it? 3) Is "finishLearning" deprecated? I tried using it but I got an error message. 4) Is it possible run swarming within the Vagrant VM? What about Cerebro? On a side note, so far I have achieved 3.3% MAPE on the test data, which would put me among the top 10 competitors (out of 26), with pretty much the basic NuPIC configuration, very similar to the hotgym example. I have experimented with 31-step predictions and 1,2,3,...,31 predictions, but this was too slow and didn't improve the results. When I finish testing all my ideas, I will post my results and experience here. Pedro. [1] http://neuron.tuke.sk/competition/index.php -- Pedro Tabacof, Unicamp - Eng. de Computação 08.
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