MATLAB: Network iterations closeloop NarX

Deep Learning Toolboxnarx

Very good again, to perform several iterations ahead with closeloop to test the network out of the sample and should proceed? Is that an iteration do with the data network today and predicted data gives me tomorrow. But if you want to keep iterating as meteria knowing I have data as inputs EMA and RSI and price I want to get closure?. thank you very much

Best Answer

Not sure what you are asking.

Have you successfully designed and evaluated the open loop net? R2trn = ? R2trna = ? R2val = ? R2tst = ?

Have you closed the loop and reevaluated? How do the closed loop R^2 compare?

Now you are ready to evaluate the closed loop net on new data?

What exactly, is the problem.

Greg

Related Solutions

MATLAB: NARX model training in the Neural Network Tool Box

Narnet and narxnet have two modes: OPENLOOP and CLOSELOOP.

The training of these and most of the MATLAB NNs is stopped via te FIRST of 6 conditions. For example,

net         = narxnet            % NO SEMICOLON

TrainParams = net.trainParam     % NO SEMICOLON

% Partial results

         trainParam: .showWindow, .showCommandLine, .show, .epochs,
                    .time, .goal, .min_grad, .max_fail, .mu, .mu_dec,
                    .mu_inc, .mu_max

TrainParams =

    Function Parameters for 'trainlm'
    Maximum Epochs                      epochs: 1000
    Maximum Training Time                 time: Inf
    Performance Goal                      goal: 0
    Minimum Gradient                  min_grad: 1e-005
    Maximum Validation Checks         max_fail: 6
    Maximum mu                          mu_max: 10000000000

The OPENLOOP mode is static and only used for training with target data fed into the feedback delay nodes. However, when it is converted to CLOSELOOP for operational use, the accuracy can be worse than if it had been directly trained in the CLOSELOOP mode.

The CLOSELOOP configuration is dynamic and is used for both training and use in the operational mode. However, CLOSELOOP training is very slow compared to OPENLOOP training and the final results may not match well with the target data.

Therefore, there are three approaches to training an operational CLOSELOOP net.

 1. Train and operate CLOSELOOP
 2. Train OPENLOOP and convert to CLOSELOOP for operational use
 3. Train OPENLOOP, convert to CLOSELOOP to train further and use for operation.

The third method tends to yield the best results.

Hope this helps

Thank you for formally accepting my answer*

Greg

MATLAB: As used in closed loop network NarX reality

I think the network is evaluated openloop and I think it gives good results,

What are I, N, ID, FD, H, Ntrn, Nval, Ntst, R2trn, R2trna, R2val and R2tst ?

but for now I do not believe them.

Why?
What do you get from the same data using closeloop?

My questions are as follows:

1 - I have evaluated the accuracy openloop, how would an in closeloop?. For a first simulation closeloop as inputs I have therefore created a matrix with the last two values InputSeries and say 30 inputs outside the training sample which would have an array of 2 * 32. To create the matrix newsTargets introduce targetSeries the last value of the array and fill with 31 NaN thereby get another matrix 1 * 32.

I don't understand where this data is coming from.

From here I simulate the network and gives me values that putting them together with real targets is a bit displaced. (I would like to send a screenshot of that graph, if you tell me in what way will send it). I think I perform the operation correctly, although I would like to evaluate the accuracy closeloop very strictly.

There is a way to see plots on ANSWERS. Fnd out how. Right now I need to see your code.

2 - My second question is because if I try the net for real, that is, suppose we are to March 4, 2013. I by a platform get data (RSI inputs and EMA), whereby I newInputs other matrix in which the last 2 introduce inputSeries values and RSI values and EMA today. (I think I'm doing well so far). To create another array newTarget do with the last value NaN targetSeries and 2 to have the same dimension as newInput (2 * 3) and newTarget (1 * 3) and performed the simulation to obtain the predicted value of the day March 5, 2013 . To continue iterating introduce the value of March 5 and get the value of the March 6, much like the network does NAR?, Is, in preparets command:

[inputs, inputStates, layerStates, targets] = preparets (net, inputSeries, targetSeries);?? and evaluate the network outside the sample.

Basically these are my doubts.

 I don't understand. Post the code.

Greg

Best Answer

Related Solutions

MATLAB: NARX model training in the Neural Network Tool Box

MATLAB: As used in closed loop network NarX reality

Related Question