I am doing time series forecasting using neural networks. I have 2 approaches:
-
Forecasting in a auto regressive manner i.e based on time series lags as shown below:
y(t) = f(y(t-1), y(t-2), ..., y(t-d))
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Forecasting in linear regression manner i.e one independent variable and one dependent variable as shown below:
y(t) = f(x(t))
In the first case, the neural network is multiple input single output, while in second case, the neural network is single input single output. The data which I am trying to forecast is wind energy production. So, in the first case, the values used are just power output. In the second case, the independent variable 'x' is wind and dependent variable 'y' is power output. In both cases, I am forecasting using sliding window method with short term horizon. In both models, there is a hidden layer in the neural network model.
I am getting low forecasting errors with the second method. I did not find any proof that neural networks can be used in this manner. So, I was hoping for some clarification from the experts here, is forecasting using neural networks correct?
Best Answer
Let me help here.
Key points:
To reduce error your model must take the "physics" into account.
If I were digging into this,
After you have done that, in order, then your MLP-NN or RBF-NN or SVM should be able to handle prediction with substantially better results.
I don't know that you have properly preprocessed your data for this particular problem. If you feed dirty data in, don't expect clean model predictions out.
There is a particular test used for evaluating the algorithmic performance of things like ensemble kalman filter or 4DVAR. I forget the name, but it assumes there is like 1.5 or 2.5 dimensional chaotic attractor. I will try to dig it up. NN's work on it too, so this test gives a clean bridge to map NN's to weather forecasting. I forget the name.
Here is a reference about using MLP in forecasting (like weather). And another. You might read this reference to help you think through the number of interior nodes.