NARX model gives high accuracy but prediction of other data is too low

I have created a NARX model using 1 external input and 1 target data set in time series. When I train the system with very long memory lengths for output, I got good accuracy for training but when I try to predict other data set from same system, prediction accuracy is very low.

then I combined 3 datasets sequencelly to train the network and then tried to predict each other seperately. let's say my dataset numbers are 1, 2 and 3, respectively. when I try to predict I got 79% accuracy for 3, 70% accuracy for 2 and 20% acccuracy for 1.

What is the reason of low accuracy for the dataset 1?

[srinivasan jayalaxmi]

sir can any one help me out as how to find accuracy of NARX models

Answer 1

[Aditya Patil]

Generally, when you get good results when training, but poor results on test dataset, it means your model is overfitting. There are several techniques to improve accuracy in such situation. To get started, check out the Improve Shallow Neural Network Generalization and Avoid Overfitting documentation.

[Murat Yetkin]

is it possible to get overfitting even for training data?

[Aditya Patil]

If you get low accuracy for training data, it means the model is not performing well.