Why do you need to split you dataset into a train set and a test set?
The reason is that when the dataset is split into train and test sets, there will not be enough data in the training dataset for the model to learn an effective mapping of inputs to outputs. There will also not be enough data in the test set to effectively evaluate the model performance.
How do you test an Arima model?
The idea is testing forecasting accuracy in general is quite simple. Simple leave out some of the data at the end of your sample and fit the model on the remaining part. Then forecast from the model. Since you have some data left, you can compare it with forecasted values.
When to split data into training and test sets?
Figure 1. Slicing a single data set into a training set and test set. Make sure that your test set meets the following two conditions: Is large enough to yield statistically meaningful results.
How to get a seasonal ARIMA model from data?
I have already modelled my data using the auto.arima () function with the external regressors as week days and traffic flow (without the Fourier terms) to get a seasonal arima model : ARIMA (3,0,3) (2,1,0) [24] with the below accuracy measures
When to use training data and testing data?
This is why it is recommended to keep training data separate from the testing data. The basic idea is to use the testing set as unseen data. After training your data on the training set you should test your model on the testing set. If your model performs well on the testing set, you can be more confident about your model.
Can you test a model with the same data twice?
This means that you can’t evaluate the predictive performance of a model with the same data you used for training. You need evaluate the model with fresh data that hasn’t been seen by the model before. You can accomplish that by splitting your dataset before you use it.