Deep Learning with Time Series and Sequence Data in order to reproduce wind data

Hello,

Thanks for the link, I spent the day trying to make the example work for me. I have learned a lot, solved some problems but I don't get a usable result and despite my efforts, I can't solve my problem. Here is the steps I followed : https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html

Here are the wind data I give to Matlab for training and what I expect in output as well (predicting the wind speed per hour over a year).

I think that learning works. The program is running well so far. When we get to the prediction phase, it doesn't work anymore. The vector YTest :

YTest = predict(net,XTest,SequencePaddingDirection="left");

The YTest vector is not complete, and "NaN" appear very quickly. I work with vectors of dimensions 8760x1 (nb hours in a year). First thing I don't understand.

Therefore the part about RMSE doesn't work, but it's not very serious.

I then tried to run the "close loop forecasting", but the figure it displays doesn't make sense. This is the most blocking point, I don't know how to fix it, the values are weird. I remind that my wind data are between 0m/s and 30m/s. Moreover there is no forecasted curve that appears.

Here is my code, if you have time to look at it to help me I will be very grateful.

load data_vent

data_vent(1:3);

numChannels = size(data_vent{1},1);

figure

tiledlayout(3,1)

for i = 1:3

nexttile

stackedplot(data_vent{i}')

xlabel("Time Step")

end

numObservations = numel(data_vent);

idxTrain = 1:floor(0.9*numObservations);

idxTest = floor(0.9*numObservations)+1:numObservations;

dataTrain = data_vent(idxTrain);

dataTest = data_vent(idxTest);

for n = 1:numel(dataTrain)

X = dataTrain{n};

XTrain{n} = X(:,1:end-1);

TTrain{n} = X(:,2:end);

end

muX = mean(cat(1,XTrain{:}),1);

sigmaX = std(cat(1,XTrain{:}),0,1);

muT = mean(cat(1,TTrain{:}),1);

sigmaT = std(cat(1,TTrain{:}),0,1);

for n = 1:numel(XTrain)

XTrain{n} = (XTrain{n} - muX) ./ sigmaX;

TTrain{n} = (TTrain{n} - muT) ./ sigmaT;

end

layers = [

sequenceInputLayer(numChannels)

lstmLayer(128)

fullyConnectedLayer(numChannels)

regressionLayer];

options = trainingOptions("adam", ...

MaxEpochs=8760, ...

SequencePaddingDirection="left", ...

Shuffle="every-epoch", ...

Plots="training-progress", ...

Verbose=0);

net = trainNetwork(XTrain,TTrain,layers,options);

for n = 1:size(dataTest,1)

X = dataTest{n};

XTest{n} = (X(:,1:end-1) - muX) ./ sigmaX;

TTest{n} = (X(:,2:end) - muT) ./ sigmaT;

end

YTest = predict(net,XTest,SequencePaddingDirection="left");

for i = 1:size(YTest,1)

rmse(i) = sqrt(mean((YTest{i} - TTest{i}).^2,"all"));

end

figure

histogram(rmse)

xlabel("RMSE")

ylabel("Frequency")

mean(rmse)

idx = 2;

X = XTest{idx};

T = TTest{idx};

figure

stackedplot(X',DisplayLabels="Channel " + (1:numChannels))

xlabel("Time Step")

title("Test Observation " + idx)

net = resetState(net);

offset = 75;

[net,~] = predictAndUpdateState(net,X(:,1:offset));

numTimeSteps = size(X,2);

numPredictionTimeSteps = numTimeSteps - offset;

Y = zeros(numChannels,numPredictionTimeSteps);

for t = 1:numPredictionTimeSteps

Xt = X(:,offset+t);

[net,Y(:,t)] = predictAndUpdateState(net,Xt);

end

figure

t = tiledlayout(numChannels,1);

title(t,"Open Loop Forecasting")

for i = 1:numChannels

nexttile

plot(T(i,:))

hold on

plot(offset:numTimeSteps,[T(i,offset) Y(i,:)],'--')

ylabel("Channel " + i)

end

xlabel("Time Step")

nexttile(1)

legend(["Input" "Forecasted"])

net = resetState(net);

offset = size(X,2);

[net,Z] = predictAndUpdateState(net,X);

numPredictionTimeSteps = 2000;

Xt = Z(:,end);

Y = zeros(numChannels,numPredictionTimeSteps);

for t = 1:numPredictionTimeSteps

[net,Y(:,t)] = predictAndUpdateState(net,Xt);

Xt = Y(:,t);

end

numTimeSteps = offset + numPredictionTimeSteps;

figure

t = tiledlayout(numChannels,1);

title(t,"Closed Loop Forecasting")

for i = 1:numChannels

nexttile

plot(T(i,1:offset))

hold on

plot(offset:numTimeSteps,[T(i,offset) Y(i,:)],'--')

ylabel("Channel " + i)

end

xlabel("Time Step")

nexttile(1)

legend(["Input" "Forecasted"])

Answer 1

[Pratik]

Hi Armel,

In my understanding, you are facing issues while getting predictions after training the model and want to figure out the reason for the same.

The code you provided and the one in the example you referred from are similar. Thus, it is highly unlikely that there is some error in the model or training. I would suggest to please check the data you are using, the format it is stored in and how you process it.

You can also try pre-processing the data to remove inconsistencies or formatting issues.

Since there is ‘NaN’ in predicted output there could be missing data or ‘NaN’ in the dataset. Working with missing data is a common task in data preprocessing. Although sometimes missing values signify a meaningful event in the data, they often represent unreliable or unusable data points.

To deal with missing data “fillmissing” function can be used, for example in the code below linear interpolation can be used to fill the missing data. Here ‘A’ is an array with missing values, ‘F’ is the filled vector.

[F,TF] = fillmissing(A,'linear','SamplePoints',x);  

Please refer to the “fillmissing” documentation for more information about the function:

https://www.mathworks.com/help/matlab/ref/fillmissing.html

Please refer to this documentation for more information about dealing with missing data:

https://www.mathworks.com/help/matlab/data_analysis/missing-data-in-matlab.html

Please refer to the following “Data Preprocessing” documentation for more information about data preprocessing:

https://www.mathworks.com/discovery/data-preprocessing.html

Hope this helps!