Neural Network - Multi Step Ahead Prediction

Question

Jack 2011-9-2

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction

评论： Mario Viola 2021-2-26

采纳的回答： Lucas García

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Hi all, please I need your help !

I've read all the posts here about Time Series Forecasting but still can't figure it out ! I'm drained.. *:-(*

I've a NARX neural network with 10 hidden neurons and 2 delays. As input I have a 510x5 (called Inputx) and as output I have a 510x1 (called Target).

I want to forecast 10 days ahead but it's really not working...

I tried the following code but I'm stuck now. *:-(*

Would you mind to help me ? *Some code will be awesome. :-(*

***////////////////////////////////////////////******** ***/////////////////////////////////////////// ******

inputSeries = tonndata(Inputx,false,false);
targetSeries = tonndata(Target,false,false);
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
[xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries);
yc = netc(xc,xic,aic);

***////////////////////////////////////////////******** ***/////////////////////////////////////////// ******

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Oleg Komarov 2011-9-2

Two things,please change the title of your post in something useful and format the code: http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup#answer_18099

Constantine 2014-11-21

with respect to the accepted answer by Lucas Garcia, I find the predicted data only agrees with the actual data as well as his every once in a while.

1. It's important, before running the fit, to clear the variables, eg. 'clear all.' Re-running without clearing the variables leads to much worse fits.

2. much better fits result from using a bigger delay, like 5, instead of the delay of 2 in his example. Or by adding additional training data, such as the time derivative or 2nd time derivatives of the training data. Of course, doing this makes the fit considerably slower.

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Answer 1

Lucas García 2011-9-7

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_20770

编辑：Lucas García 2015-9-3

在 MATLAB Online 中打开

Hi Jack,

When using narxnet, the network performs only a one-step ahead prediction after it has been trained. Therefore, you need to use closeloop to perform a multi-step-ahead prediction and turn the network into parallel configuration.

Take a look at this example for a multi-step-ahead prediction, N steps. This uses the dataset magdata.mat which is available in the Neural Network Toolbox. Also, some of the inputs will be used for performing the multi-step-ahead prediction, and results validated with the original data. I hope the comments help to understand.

Edited in September 2015 to simplify step 5

%% 1. Importing data

S = load('magdata');
X = con2seq(S.u);
T = con2seq(S.y);

%% 2. Data preparation

N = 300; % Multi-step ahead prediction
% Input and target series are divided in two groups of data:
% 1st group: used to train the network
inputSeries  = X(1:end-N);
targetSeries = T(1:end-N);
% 2nd group: this is the new data used for simulation. inputSeriesVal will 
% be used for predicting new targets. targetSeriesVal will be used for
% network validation after prediction
inputSeriesVal  = X(end-N+1:end);
targetSeriesVal = T(end-N+1:end); % This is generally not available

%% 3. Network Architecture

delay = 2;
neuronsHiddenLayer = 10;
% Network Creation
net = narxnet(1:delay,1:delay,neuronsHiddenLayer);

%% 4. Training the network

[Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai); 
% Performance for the series-parallel implementation, only 
% one-step-ahead prediction
perf = perform(net,Ts,Y);

%% 5. Multi-step ahead prediction

[Xs1,Xio,Aio] = preparets(net,inputSeries(1:end-delay),{},targetSeries(1:end-delay));
[Y1,Xfo,Afo] = net(Xs1,Xio,Aio);
[netc,Xic,Aic] = closeloop(net,Xfo,Afo);
[yPred,Xfc,Afc] = netc(inputSeriesVal,Xic,Aic);
multiStepPerformance = perform(net,yPred,targetSeriesVal);
view(netc)
figure;
plot([cell2mat(targetSeries),nan(1,N);
      nan(1,length(targetSeries)),cell2mat(yPred);
      nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs')

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Lucas García 2011-9-8

Hi Jack,

In my example, I created two sets of inputs and targets. The arrays "inputSeries" and "targetSeries" are used for training the network. These are your 5x510 and 1x510 arrays, respectively.

In order to do a multi-step-ahead prediction, you need to provide the network with NEW inputs. If you are doing a 10-step-ahead prediction, it will be a 5x10 array of inputs. Since I didn't have a new set of inputs in my dataset, I used part of the inputs ("inputSeriesVal") for doing the prediction process, and checked how good the network was performing by plotting the predictions with the real outputs ("targetSeriesVal").

Some rearranging of data has to be done though, since you also need to provide the network with the last d inputs and targets from the training dataset (d is the number of delays):

- in the case of the inputs, your array for prediction will be the last d values from the training dataset (5xd) and the new 10 inputs (with size 5x(d+10)). Named "inputSeriesPred" in the example.

- in the case of the targets, you will provide the last d values from the training dataset (1xd) and 10 values of NaNs (with size 1x(d+10)). Named "targetSeriesPred" in the example.

Nilanjan 2013-4-25

在 MATLAB Online 中打开

Hi, i am using NARX to predict a daily stock market index data (Sensex 2003x1 matrix) as target and another daily stock market data(Nifty) as input. I have done it using the example you have shown in:

http://www.mathworks.in/matlabcentral/answers/14970

The code:

%%%newNARX code 24/4/2013
%%1. Importing data
% Matrix of 2003x1 each are
% daily stock market indices data
% of Nifty & Sensex
load Nifty.dat;
load Sensex.dat;
% %%S = load('magdata');
% %%X = con2seq(S.u);
% %%T = con2seq(S.y);
% To scale the data it is converted to its log value:
  lognifty = log(Nifty);
  logsensex = log(Sensex);
    X = tonndata(lognifty,false,false);
    T = tonndata(logsensex,false,false);
  %  X = con2seq(x);
  %  T = con2seq(t);
%%2. Data preparation
N = 300; % Multi-step ahead prediction
% Input and target series are divided in two groups of data:
% 1st group: used to train the network
inputSeries  = X(1:end-N);
targetSeries = T(1:end-N);
% 2nd group: this is the new data used for simulation. inputSeriesVal will 
% be used for predicting new targets. targetSeriesVal will be used for
% network validation after prediction
inputSeriesVal  = X(end-N+1:end);
targetSeriesVal = T(end-N+1:end); % This is generally not available
%%3. Network Architecture
delay = 2;
neuronsHiddenLayer = 50;
% Network Creation
net = narxnet(1:delay,1:delay,neuronsHiddenLayer);
%%4. Training the network
[Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai); 
% Performance for the series-parallel implementation, only 
% one-step-ahead prediction
perf = perform(net,Ts,Y);
%%5. Multi-step ahead prediction
inputSeriesPred  = [inputSeries(end-delay+1:end),inputSeriesVal];
targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];
netc = closeloop(net);
view(netc)
[Xs,Xi,Ai,Ts] = preparets(netc,inputSeriesPred,{},targetSeriesPred);
yPred = netc(Xs,Xi,Ai);
perf = perform(net,yPred,targetSeriesVal);
figure;
plot([cell2mat(targetSeries),nan(1,N);
      nan(1,length(targetSeries)),cell2mat(yPred);
      nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs')

Network predictions are coming very bad.. I guess there is some problem with the close loop's initial input states and initial layer states. please help.

Nils 2020-5-25

"inputSeriesVal = X(end-N+1:end)"

What could be done if these values are not available?

Chris P 2020-8-2

I'm wondering the same thing as Nils. What if this is implemented in real-time and we don't have these inputs planned in advance? For instance if we are trying to run an optimal control scheme to determine the best input values to achieve a desired trajectory. Or if some of the NN inputs are uncontrollable but necessary to get a good model prediction.

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Answer 2

Mark Hudson Beale 2011-9-9

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_21002

在 MATLAB Online 中打开

Here is an example that may help. A NARX network is trained on series inputs X and targets T, then the simulation is picked up at the end of X using continuation input data X2 with a closed loop network. The final states after open loop simulation with X are used as the initial states for closed loop simulation with X2.

% DESIGN NETWORK
[x,t] = simplenarx_dataset;
net = narxnet;
[X,Xi,Ai,T] = preparets(net,x,{},t);
net = train(net,X,T,Xi,Ai);
view(net)
% SIMULATE NETWORK FOR ORIGINAL SERIES
[Y,Xf,Af] = sim(net,X,Xi,Ai);
% CONTINUE SIMULATION FROM FINAL STATES XF & AF WITH ADDITIONAL
% INPUT DATA USING CLOSED LOOP NETWORK.
% Closed Loop Network
netc = closeloop(net);
view(netc)
% 10 More Steps for the first (now only) input
X2 = num2cell(rand(1,10));
% Initial input states for closed loop continuation will be the
% first input's final states.
Xi2 = Xf(1,:);
% Initial 2nd layer states for closed loop contination will be the
% processed second input's final states.  Initial 1st layer states
% will be zeros, as they have no delays associated with them.
Ai2 = cell2mat(Xf(2,:));
for i=1:length(net.inputs{1}.processFcns)
  fcn = net.inputs{i}.processFcns{i};
  settings = net.inputs{i}.processSettings{i};
  Ai2 = feval(fcn,'apply',Ai2,settings);
end
Ai2 = mat2cell([zeros(10,2); Ai2],[10 1],ones(1,2));
% Closed loop simulation on X2 continues from open loop state after X.
Y2 = sim(netc,X2,Xi2,Ai2);

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Elma 2014-2-18

在 MATLAB Online 中打开

I have tried this code, and it is great, but when I try to apply it for my problem, I get really bad results. I tried with changing input and feedback delays, as well as number of hidden neurons, but the results are always bad (figure) (green line is multi step predistion)

The code is given below:

% DESIGN NETWORK
ID=1:2;
HL=6
FD=1:2;
net = narxnet(ID,FD,HL);
[X,Xi,Ai,T] = preparets(net,x,{},WS);
net.divideFcn = 'divideblock';  
net = train(net,X,T,Xi,Ai);
% SIMULATE NETWORK FOR ORIGINAL SERIES
[Y,Xf,Af] = sim(net,X,Xi,Ai);
% CONTINUE SIMULATION FROM FINAL STATES XF & AF WITH ADDITIONAL
% INPUT DATA USING CLOSED LOOP NETWORK.
% Closed Loop Network
netc = closeloop(net);
Xi2 = Xf(1,:);
Ai2 = cell2mat(Xf(2,:));
for i=1:length(net.inputs{1}.processFcns)
  fcn = net.inputs{i}.processFcns{i};
  settings = net.inputs{i}.processSettings{i};
  Ai2 = feval(fcn,'apply',Ai2,settings);
end
Ai2 = mat2cell([zeros(10,2); Ai2],[10 1],ones(1,2));
Y2 = sim(netc,X2,Xi2,Ai2);
plot(1:length(WS),cell2mat(WS))
hold on
plot(1:length(Y),cell2mat(Y),'r')
plot(length(WS):length(WS)+length(Y2)-1,cell2mat(Y2),'g')
legend('Input data - target series','One-step ahead prediction','Multi-step prediction beyond target series');

WT 2015-3-1

May I know what this "Ai2 = mat2cell([zeros(10,2); Ai2],[10 1],ones(1,2));" means?

Thank You

IOANNIS4 2015-8-5

Please can someone exlpain little bit more this part

% Xi2 = Xf(1,:); Ai2 = cell2mat(Xf(2,:)); for i=1:length(net.inputs{1}.processFcns) fcn = net.inputs{i}.processFcns{i}; settings = net.inputs{i}.processSettings{i}; Ai2 = feval(fcn,'apply',Ai2,settings); end Ai2 = mat2cell([zeros(10,2); Ai2],[10 1],ones(1,2)); Y2 = sim(netc,X2,Xi2,Ai2);

Please you would really help us, Kind regards, Ioannis

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Answer 3

Greg Heath 2014-3-25

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_130165

When the loop is closed, the net should be retrained with the original data and initial weights the same as the final weights of the openloop configuration.

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Mario Viola 2021-2-26

Just one question, How can i access the final weights from the open loop configuration ? ANd how to set them in the new closed configuration ?

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Answer 4

mladen 2013-10-25

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_112656

Be aware that predicting outputs this way (similar to cascade relaization of linear system) has great sensitivity to parametar estimation errors because they propagate in the process Mark Hudson Beale mentioned. This is highlighted in hard, multiple steps ahead problems.

Parallel realizations (simoltanoius output estimation...for instance 10 outputs of neural network for next 10 time steps) tend to be less sensitive to this errors. I have implemented this with my code which is alway prone to error :) So my subquestion is:

Is there some specific way to prepare my data for training with some matlab function?

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MAT-Magic 2020-3-27

@Lucas: Thanks!!

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Answer 5

Murat Akdag 2015-3-28

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_173034

I'm trying to understand this narnet but still can't solve. Looking for help in matlab help section in this page: http://www.mathworks.com/help/nnet/ug/multistep-neural-network-prediction.html?searchHighlight=narnet%20multistep i try same codes but there is an error in >> [netc,xi,ai] = closeloop(net,xf,af); too many arguments. I just need one working sample about narnet which can predict 12 steps ahead prediction. I try to do with GUI in matlab NN section. But it has predict just 1 step ahead with removedelay command. I need 12 steps ahead. Thanks for help.

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Greg Heath 2017-7-11

epochs are the number of loops the training goes through to trying to minimize the objective function ...

but you knew that already because you have GOOGLE & WIKIPEDIA

RIGHT?

Did you find this

https://www.quora.com/What-is-epochs-in-machine-learning

?

Charles 2017-7-12

编辑：Charles 2017-7-12

yes, i was being lazy, and eventually found the answer. Still working on my currency trend prediction NARX.....looking to establish if next days price will in fact be higher or lower than last price....so not so much trend.

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Answer 6

hugo kuribayashi 2015-4-15

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https://ww2.mathworks.cn/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction#answer_175279

Considering all this examples.. How can i calculate MAPE error instead MSE?

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Greg Heath 2017-7-11

编辑：Greg Heath 2017-7-12

You mean "in addition to" ?

1. Learn with MSE or MSEREG

2. Report your findings with whatever floats your boat.

3. I prefer NMSE [0 1] for regression and time series

and

PCTERR [ 0 1 ]

for classification and pattern recognition

(;>)

Greg

P.S. Be aware of the shortcomings of MAPE and it's attempted modifications

https://en.wikipedia.org/wiki/Mean_absolute_percentage_error

Hope this helps.

Greg

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