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I have been using the Encog Neural Net workbench (version 3.2) to run the sunspot prediction routine and have noticed that when changing the future prediction window to greater than 1 the results in the sunspot_output.csv appear to be time offset so that the output when the network evaluates at t=0 are not really (t+1), (t+2), (t+3) etc. It's very likely I'm not understanding how the workbench is displaying the results so perhaps someone could clarify this for me.

As I understand it if you use a past window of 30 and a future window of 14 then the network will look at the last 30 records and predict forward from the last available record (in this case lets say 11/1/1951 is the last available record). So an evaluation on 11/1/1951 will look back 30 records to 5/1/1949 and use this information to feed through the trained network to predict data for 12/1/1951 (t+1), 1/1/1952 (t+2), 2/1/1952 (t+3), etc. However, looking at the result file this does not appear to be the case. The "prediction" really appears to be a repeat of the pattern from the previous 14 records. So that (t+1) is really more representative of (t-14) 08/01/1950 than the next record forward from (t=0) which would be on 12/1/1951.

I have an image that shows this but unfortunately I don't appear have the reputation points to post it yet. To reproduce this issue I suggest using the Encog workbench and using a past window of 30, future window of 14 and training error of 1 or 2%.

To Summarize:

  1. Has anyone else noticed this issue when looking at the predictive network results, particularly for greater than one time step ahead?
  2. Why do the workbench results show that the encog predictive neural network is not properly predicting into the future when you look at the dates associated with the outputs.

Thank you for any thoughts you may have!

janisz
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dnvwind
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1 Answers1

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That's not an issue is how a sliding window time series forecaster works. I would suggest you to deepen here https://www.cs.rutgers.edu/~pazzani/Publications/survey.pdf It really depends on how you tune the neural network.

If you want more predictive power you have to extract features or syntetize new features (for example I would use wavelet extraction and denoising).

Pay attention to normalization. Use range normalization if you know that there are known ranges otherwise z-normalization. Use the proper activation function: Sigmoid if the normalized range is 0,1 or tanh if the range is -1,1.

But before ending that the neural network is not predicting I would suggest you to use SVR (Support Vector Regression) included in encog. It guarantees (if it is present) to reach the global minimum. See if the SVR predicts better than the ANN. If not use my firsts suggestions ;-)

Vincenzo

vincenzodentamaro
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