Neural Network - Multi Step Ahead Prediction
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I am very new to matlab.
Objective: To generate a future annual average, min and max temperature data (up to 2022) from existing data range (1983 - 2014) Existed Data arrangement in excel sheet:
Year Ta Tmax Tmin
1983 28 34 23
1984 28.1 34.2 24.2
.... ... .... ...
2014 29 35 24
I tried to use nnstart tool (time series); unfortunately, I don't have the knowledge to generate future data (up to 2022)
Please help me
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Greg Heath
2016-5-6
编辑：Greg Heath
2016-5-6
1. The final OL NARNET model should have 3 dimensional inputs and outputs.
2. The final CL NARNET model will have 3 outputs which are fed back to the input.
3. However, I would first see what happens with the 3 individual series
4 a.Use DIVIDEBLOCK with the default trn/val/tst ratios 0.7/0.15/0.15.
b. Use the mean and std of the trn subset to normalize
the whole series
c. Use the autocorrelation function of the trn subset
to determine the statistically significant lags (i.e.,
the lags at which the absolute values of the
autocorrelation function exceed the 95th percentile of
Gaussian random noise)
d. Use as few hidden nodes as possible to design a
separate OL NARNET model with
NMSEtrn = mse(ytrn-ttrn)/mean(var(ttrn',1)) < 1e-4
e. Use the training record tr to obtain NMSE for the
separate trn/val/tst subsets
f. Close the loop and obtain the resulting CL
performances
(trn,val,tst and total)
g. If the CL performance is unsatisfactory, train netc
intialized with the final weights of neto.
5. Once familiarized with the single series problem,
try some of the 2-input/2-output problems. The
techniques are similar EXCEPT
there will be two feedback signals. Therefore, the significant
lags of the corresponding CROSS-CORRELATION functions may have
to be taken into consideration.
6. It is a relatively complicated problem so let us know if
there are difficulties.}
Hope this helps.
*Thank you for formally accepting my answer*
Greg
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