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
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.
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