Which weights will be used for predicting the future response?

I am doing work on time series data prediction.

The input signal is the daily concentration of dust particles in the air and having format (10x24), 10 =days and for each day 24 values, then it is converted to row vector of (1,240) by using

input = imresize(dust, [1, 10*24]); % converts matrix into vector

For training my network, I have made the model (3:5:1)(tanh, tanh)(0.05)(1)(500),

where 3= inputs, 5 hidden layer neuron, 1 output layer, (tanh tanh) transfer function for input- hidden layer, and hidden-output layer, the learning rate is 0.05, 1= bias and iterations are 500. I get trained network and tracking was absolute.

Which layer weights will be used in prediction for future response (i.e input-hidden layer or hiden-output layer,) as their dimensions are

Input to hidden layer= inputweights开发者_StackOverflow社区(input,hidden)= 3 x 5 matrix hidden to output layer = outputwhts(output,hidden)= 1x5 row vector.

I want to predict the 24 values prediction and 168 value prediction based on my input data weights.

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Both the input-hidden and the hidden-output weights are involved in the prediction. Predictions made by neural networks always rely on all of the weights in the network.

Now, it sounds as though you want to use a network trained on examples of size 24 to predict examples of size 168, and that's why you want to know which set of weights to keep.

If that is the case (its a little unclear), then the bad news is that in general, you can't change the size of a network's input or output without completely retraining it.

My advice would be to calculate what 168 value example would be analogous to each 24 value example and train a network on those examples.