Unable to inverse_transform the value of feature because of different dimensionality

Question

I'm designing a multivariate time series model. For that I'm inputing 5 features to lstm model and try to predict the output of 1 variable(i.e. whose value is dependent on itself and other 4 features).

For that I'm doing the feature scaling as follows:-

#Features Scaling
`from sklearn.preprocessing import MinMaxScaler
 sc = MinMaxScaler(feature_range = (0,1))
 training_set_scaled = sc.fit_transform(training_set)
 print(training set scaled)`
   

Output:-

At the output of the model, I got the predicted value as:

However, when it tried to inverse transform it as:

predicted_stock_price = sc.inverse_transform(predicted_stock_price)

I got the the following error:-

non-broadcastable output operand with shape (65,1) doesn't match the broadcast shape (65,5)

Please help. Thank you in advance :)

Answer 1

The problem is that you use sc to min-max-scale the five features. Therefore, sc can also only be used to inverse transform the scaled version of the features (shown by you as output), which would give you back the original feature values. The label (model output) is independent from that. You can also, but do not necessarily have to scale your dependent variable, and certainly not with the same scaler object.