Asked by Shashank Bhatia
on 7 Aug 2018

I am using neural network time series to predict stock price for next week. Normal procedure to approach this problem as explained in nnhelp isn't sufficient. Errors are big and results are absurd. Going through online help, I have seen various answers by Greg which are somewhat helpful but really time consuming. If Greg can write the process for X = phInputs;T = phTargets; example it would be really enlightening. citing steps for 1. Normalize the data (making a time-series stationary), 2. Choosing hidden layer number, 3. Feedback delay number, 4. Data classification (using datablock)

Answer by Greg Heath
on 7 Aug 2018

Accepted Answer

Can Greg post an example for using neural network time series in the whole sense?

Yes he can … and in fact, he has! If the examples are not in ANSWERS, then they must be in COMP.SOFT-SYS.MATLAB!

It may sound strange, but I would first search using the terms

greg ph

If still bewildered, I'm sure you can find similar posts using other MATLAB time-series sample data.

help nndatssets

doc nndatasets

Thank you for formally accepting my answer

Greg

Shashank Bhatia
on 7 Aug 2018

Shashank Bhatia
on 7 Aug 2018

Greg Heath
on 7 Aug 2018

As you know, I have posted hundreds of NN posts over the years in this and other groups. At this point I have no idea where particular posts are. Also, it is rare that only one of the posts is relevant to a particular question. All I can do is suggest reasonable search words.

The reference nndatasets is where the MATLAB sample data is. It makes no sense for me to spend my time on other data.

Typically, there is more than one relevant reference. I'll check that reference and get back to you.

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Answer by Greg Heath
on 9 Aug 2018

Edited by Greg Heath
on 9 Aug 2018

Q1a: Do I need to normalize/standardize the data before feeding to neural network?

A1a: Typically, Yes. One of the following

Normalization : range = [ 0,1 ]

Standardization: [mean, variance] = [ 0,1 ]

Q1b: Or does neural network take care for standardization of data

A1b: MATLAB automatically normalizes

I prefer to standardize

Q2: How can I decide range of the data to be used? 5 yrs or 10 years? Is the process of doing so manual observing model mse?

A2: Always plot the data before making any decisions.

Then decide what model(s) might be appropriate.

You may have to use different models in different

ranges.

Q3: How can I decide number of hidden layers, FD (feedback delays)? Is the process manual?

A3a: One hidden layer is always sufficient.

Specific knowledge of the data may warrant

two. I determine number of hidden nodes by

trial and error.

A3b. I determine characteristic delays from the

auto and crosscorrelation functions

Q4: After making the network with sufficiently accurate mse, do I need to convert the net into closed-loop (netc) for next week prediction?

A4. It depends on which time-series model that you are using.

If it is a feedback model it should be obvious that you need to

close the loop to predict the future.

A4: Yes. I have posted more than a sufficient number

of tutorials in the NEWSGROUP and ANSWERS.

Hope this helps.

*Thank you for formally accepting my answer*

Greg

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Answer by Greg Heath
on 9 Aug 2018

Q1a: Do I need to normalize/standardize the data before feeding to neural network?

A1a: Typically, Yes. One of the following

Normalization : range = [ 0,1 ]

Standardization: [mean, variance] = [ 0,1 ]

Q1b: Or does neural network take care for standardization of data

A1b: MATLAB automatically normalizes

I prefer to standardize

Q2: How can I decide range of the data to be used? 5 yrs or 10 years? Is the process of doing so manual observing model mse?

A2: Always plot the data before making any decisions.

Then decide what model(s) might be appropriate.

You may have to use different models in different

ranges.

Q3: How can I decide number of hidden layers, FD (feedback delays)? Is the process manual?

A3a: One hidden layer is always sufficient. Specific

knowledge of the data may warrant two. I determine

number of hidden nodes by trial and error.

A3b. I determine characteristic delays from the auto

and crosscorrelation functions

Q4: After making the network with sufficiently accurate mse, do I need to convert the net into closed-loop (netc) for next week prediction?

A4: Yes. I have posted more than a sufficient number

of tutorials in the NEWSGROUP and ANSWERS.

Hope this helps.

*Thank you for formally accepting my answer*

Greg

Shashank Bhatia
on 10 Aug 2018

Thanks for answering. This has been great guidance.

Coming directly to the further questions:

FURTHER QUESTION

Following are the dimensions of NN used. I have no idea how to fix below items: numLayers: 2

numOutputs: 1

numInputDelays: 26

numLayerDelays: 0

numFeedbackDelays: 0

numWeightElements: 20

sampleTime: 1

How these parameters are considered mathematically inside MATLAB?

Searching through NEWSGROUP page, I have found following link to determine ID,FD... but how to decide other factors (layer numbers etc)?

https://groups.google.com/forum/#!searchin/comp.soft-sys.matlab/$20greg$20ph%7Csort:date/comp.soft-sys.matlab/DjDQXsa7wNE/cRhNT1RcBQAJ

In continuation to our discussion

Ans 1: Standardization: [mean, variance] = [ 0,1 ] --> Done

Ans 2: Always plot the data before making any decisions. Then decide what model(s) might be appropriate. You may have to use different models in different ranges. --> Understood. In my case I am considering past 1 year data for predicting 1 week ahead stock price closing.

Ans 3:

Below is my code:

n=length(data); %10 years data

num=200; % ~1 year data

nextN=5; % to predict next 1 week stock closing

ntrain=n-num;

datatrain=data(ntrain-num:ntrain);

mu = mean(datatrain); %mean closing price of 1 year

sig = std(datatrain); %standard deviation closing price of 1 year

sdatat = (datatrain - mu) / sig; %standarization of data

T=tonndata(sdatat(1:end,1),false,false);

delay=[1:26];

hiddenlayersize=20;

trainFcn='trainlm'

net=narnet(delay,hiddenlayersize,'open',trainFcn);

net.input.processFcns={'removeconstantrows','mapminmax'}

[Xs,Xi,Ai,Ts]=preparets(net,{},{},T);

rng('default')

net=train(net,Xs,Ts,Xi,Ai);

[Ys,Xf,Af]=net(Xs,Xi,Ai);

[netc,Xic,Aic]=closeloop(net,Xf,Af);

nextweek=cell(0,nextN);

Ypred=netc(nextweek,Xic,Aic);

Ypred=transpose(cell2mat(Ypred));

Ypred=Ypred*sig+mu; %Predicted value for 1 week ahead

Ans 4: I have read your posts. Thanks once again.

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Answer by Shashank Bhatia
on 8 Aug 2018

Time-series I am working with looks like as per the attachment.

Background: I am using neural network time series to predict stock price (of the attached picture) for next week.Clearly, data is not stationary (time-invariant).

My questions:

Q1: Do I need to normalize/standardize the data before feeding to neural network? Or does neural network take care for standardization of data Q2: How can I decide range of the data to be used? 5 yrs or 10 years? Is the process of doing so manual observing model mse? Q3: How can I decide number of hidden layers, FD (feedback delays)? Is the process manual? Q4: After making the network with sufficiently accurate mse, do I need to convert the net into closed-loop (netc) for next week prediction?

Thanks in advance.

Greg Heath
on 9 Aug 2018

I just started reading the above. Will respond as soon possible (am busy on other things now 22:16PM)

Greg

Greg Heath
on 12 Aug 2018

The only thing different about your case is the dominant low order (quadratic, cubic,...?) polynomial trend.

Subtracting that out should yield a decent long term predictor.

Then you can use the remainder for short term predictions.

Greg

Shashank Bhatia
on 13 Aug 2018

With autocorreletion technique I am getting delay value >1000 of my training data number (~2400). For such a high value of delay, required number of nodes is still unclear.

I have used 6 nodes, which is giving me good results (MSE<e-9 for standarized data for open loop) but not always.

FURTHER QUESTIONS

1. How to get correct value of Hidden nodes (any approximation would suffice to start with).

2. Right now, I am not training the closed loop or removing delays as you can see in above code, Do I really need to?

Sincerely,

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## Image Analyst (view profile)

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## Greg Heath (view profile)

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## Shashank Bhatia (view profile)

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## KAE (view profile)

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