LSTM-多元时间序列预测

Question

I was reading the tutorial on Multivariate Time Series Forecasting with LSTMs in Keras https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/#comment-442845

I have followed through the entire tutorial and got stuck with a problem which is as follows-

In this tutorial, the train and test splits have 8 features viz., 'pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain' at step 't-1', while the output feature is 'pollution' at current step 't'. This is because, the framing of the dataset as a supervised learning problem is about predicting the 'pollution' at current hour/time step 't', given the pollution and weather measurements at the prior hour/time step 't-1'

After fitting the model to the training and testing data splits, what if I want to make predictions for a new dataset having 7 features since it does not have 'pollution' feature in it and I explicitly just want to predict for this one feature using the other 7 features.

Thanks for your help!

How do I handle such a situation? (while the remaining 7 features remain the same)

Edit- Assume that my dataset has the following 3 features while training/fitting the model- shop_number, item_number, number_of_units_sold

AFTER, I have trained the LSTM model, I get a dataset having the features- 'shop_number' AND 'item_number'. The dataset DOES NOT have 'number_of_units_sold'.

The 'input_shape' argument in 'LSTM' has 1 as time step and 3 as features while training. But while predicting, I have 1 time step but ONLY 2 features (as 'number_of_units_sold' is what I have to predict).

So how should I proceed?

Answer 1

If pollution is the last feature:

X = original_data[:,:,:-1]
Y = original_data[:,:,-1:]

If pollution is the first feature

X = original_data[:,:,1:]
Y = original_data[:,:,:1]

Else

i = index_of_pollution_feature
X = np.concatenate([original_data[:,:,:i], original_data[:,:,i+1:],axis=-1)
Y = original_data[:,:,i:i+1]

Make a model with return_sequences=True , stative=False and that's it. Don't use Flatten, Global poolings or anything that removes the steps dimension.

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If you don't have any pollution data at all for training, then you can't.