I have a dataset of the daily temperature for a couple of years. The data is in the interval form, including daily high temp and daily low temp.
I want to do a forecasting of the data, and I recently read several paper mention that the multilayer perceptron have the advantage to do this. However, after reading the paper I still got puzzled. I know in order to conduct it, I will need to have input, hidden layer and output. But in Matlab, though I have the code already, I still don't know how to simulate it. What should I put as its input and output, should I put the interval data as the input and output? And how can I choose hidden layer?
The input in an MLP network is the input feature data that you are trying to predict the outcome of. The output is what you are trying to predict. For the hidden layer that will determine how well it predicts, which you want as large as it needs to achieve reasonable prediction results. Going too large and it just memorizes the data rather than generalize on a pattern when training is run.
For example, if your input layer would be what day of the year it is (1-365), what the high was of the day, and what the low is of the day. And I assume is what the high and low temperature would be for the next day?
The more relevant input features you have the better the network will be.