Training Neural Network on Discontinuous Timeseries
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I'm trying to build multilayer feedforward network for timeseries forecasting.
These are my current available data: * Dataset #1: Feb - Apr * Dataset #2: June - Sept * Dataset #3: Oct - Dec
I have successfully built a NN based on Dataset #1, however, I wish to increase the accuracy of my network prediction by expanding the training to include dataset 1-3.
Would it be safe to append all datasets into a single martix, given that they are disconnected in time? (i,e MATRIX(1:60,:) --> dataset #1, MATRIX(61:181,:) --> dataset #2, MATRIX(182:272,:) --> dataset #3)
Or can I simply retrain my neural network three times by repeating the training line in the code, each time with a different dataset input/output? (Example in the code below)
%Create MLP Network
MLPNetwork=feedforwardnet(i,'trainlm');
MLPNetwork.trainparam.min_grad = 0.00000001;
MLPNetwork.trainParam.epochs = 10000;
MLPNetwork.trainParam.lr = 0.01;
%MLPNetwork.trainParam.max_fail =100;
MLPNetwork.divideParam.trainRatio = 70/100;
MLPNetwork.divideParam.valRatio = 15/100;
MLPNetwork.divideParam.testRatio = 15/100;
MLPNetwork=train(MLPNetwork,dataset1_input,dataset1_output);
MLPNetwork=train(MLPNetwork,dataset2_input,dataset2_output);
MLPNetwork=train(MLPNetwork,dataset3_input,dataset3_output);
%simulation%
Simulation_1=MLPNetwork(dataset1_input)
performance_1 = perform(dataset1_output,Simulation_1)
Simulation_2=MLPNetwork(dataset2_input)
performance_2 = perform(dataset2_output,Simulation_2)
Simulation_3=MLPNetwork(dataset1_input)
performance_3 = perform(dataset3_output,Simulation_3)
Little background on the nature of the datasets: The inputs are hourly weather data (Atmospheric pressure, wind speed.. etc) and the output is hourly water level. Hence datasets from different seasons are important.
Many thanks.
5 件のコメント
Greg Heath
2019 年 7 月 21 日
編集済み: Greg Heath
2019 年 7 月 21 日
Nada,
The "direct relationship" you have described is not a timeseries configuration.
Hope this helps.
Greg
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