Statsforecat.predict expects wrong dataframe shape on X_df

[Vitorbnc]

Statsforecat.predict expects wrong dataframe shape on X_df

[elephaint]

@elephaint but I would like to do the following:

• Fit the model on training data
• Predict h steps after a given input sequence other than the training period (which I was assuming was the role of X_df)

How can I supply an unseen input sequence to the model and get it to predict the next sequence?

In an ARIMA model, we train on [x_t, x_{t+1}, ...., x_{t + T}] for a particular time series. We can then make predictions for that series only, for an arbitrary long period following x_{t + T} (by setting the horizon in our predict function). We can optionally add exogenous variables during training, and during the prediction period (the latter by including them in X_df for all dates in your forecasting horizon).

You can't 'supply an unseen input sequence' to an ARIMA model. If you have an unseen input sequence, you would normally train an ARIMA model on that unseen input sequence, and subsequently create forecasts for a horizon using that newly trained model. Each new time series requires a new ARIMA model.

[Vitorbnc]

Actually the quick fix expected_shape = (h * len(self.ga), self.ga.data.shape[1] + 2) gets rid of the error but the predictions won't update if passing a different slice of the dataframe in X_df

[jmoralez]

Hey. The X_df argument is for future values of exogenous features and you don't have any, so you don't need to provide it.

[Vitorbnc]

@jmoralez so what is the correct way to predict future values for some input other than the training dataframe?

[elephaint]

@Vitorbnc You can just run predict(h=12, level=[90]). That will give you the predictions for the timeperiod following the training period.

[Vitorbnc]

@elephaint but I would like to do the following:

• Fit the model on training data
• Predict h steps after a given input sequence other than the training period (which I was assuming was the role of X_df)

How can I supply an unseen input sequence to the model and get it to predict the next sequence?

[Vitorbnc]

@elephaint ok then, that makes sense. Thanks for the explanation. I am trying to do the same with mlforecast, but I am getting a different error:

I am sure there is no NaN neither in the train nor the test dataframes though

[elephaint]

Thanks - it's very hard to debug based on this picture only. Can you share a minimal working example of your code?

Based on the picture I can only suggest to double check the existence of NaN in train_df.

Note that you are training and testing on the same timestamps - I assume that is on purpose (as it's something that you'd want to avoid normally in forecasting)? I.e. you're supplying the full train_df as training set, and use a subset of train_df as test set. Hence, any test results you get will not be representative for the actual forecasting performance. I.e. normally one would do something like this:

train_df = df[:-12]
test_df = df[-12:]

in order to properly separate train- and test sets.