Ameya Deoras

Jack, there are two places where prediction is shown. In loadForecastNN, look for the line "forecastLoad = sim(net, testX')';". Please also take a look at my comment from 20 May 2011 for an example on using the function forecastLoad to create a prediction.

There are 3 ways of doing multi-step prediction:
1. You can build a NARX network. There are examples in the documentation that show how to do multi-step with such a network.
2. You can build a feedforward network designed for predicting N steps ahead. That is what I do in this example - build a network for 24 step-ahead prediction. This is done by lagging the inputs 24 observation. The function createLags above will do this for you.
3. Run the prediction in a loop. The function multiPredict above does this for a 1-step network. In the context of this example, you can use the N-step model to do 2N, 3N, 4N... step prediction.

While I don't think the lack of multi-step prediction warrants a 1-star rating, you are of course entitled to rate it based on whatever criteria make sense to you. This is not intended to be a tutorial on Neural Networks Toolbox so I can't possibly include everything there is to know about using it. I encourage you to consult the documentation or contact technical support for help with your specific needs.

Here is a function that can be used to create an arbitrary predictor matrix with any number of lags:

function mat = createLags(vec, lags)
% CREATELAGS generates lagged versions of an input vector or matrix to
% generate a predictor matrix.
%
% USAGE:
% predictorMatrix = createLags(series, lags)
%
% Here series is a numObs-by-numDim matrix of observations. If numDim > 1,
% it implies the input series is a multidimensional series. lags is a
% vector of integer lags where 0 corresponds to no lag, +1,+2,+3... correspond to
% lags of 1,2,3... steps, and -1,-2,-3 correspond to "leads" of 1,2,3
% steps. predictorMatrix is a numObs-by-numDim*numLags matrix of the
% shifted versions of the input matrix

% x = [1 2 3 4; -1 -2 -3 -4]'
% y = createLags(x, [-1 0 2])

[numObs, numDim] = size(vec);
numLags = length(lags);
mat = NaN(numObs, numDim * numLags);
for i = 1:length(lags)
    mStaInd = max(1, lags(i)+1);
    mEndInd = min(numObs, lags(i)+numObs);
    vStaInd = max(1, 1-lags(i));
    vEndInd = min(numObs, numObs-lags(i));
    
    mat(mStaInd:mEndInd, (i-1)*numDim+1:i*numDim) = vec(vStaInd:vEndInd,:);
end

Hi Ameya, When I try to use this forecaster, NeuralNet forecasts to Zero load all the day, However the other model shows the forecast load. I tried for several days all the time it is the same case where NN shows zero load. Is there anything that I can do to get NN work?

I'm plotting data that is date/time stamped, I have data of six hour per day, 600 days, when my data is plotted, between one day and next I find a lot of dead space, It's like the hour I don't have the date were there. Do you know how can I eliminate those dead space? and use Intelligent Dynamic.

Thanks