I understand that you want to implement LSTM network to predict wind speed by giving multiple input features.
The error message you received suggests that there's a mismatch between the number of features in the LSTM layer and the input data used for prediction.
The problem you have described is not technically multiple-input RNN problems. This is because what we have is data that is described by multiple features - in the case of a single feature (and a single observation for simplicity), you would feed your RNN a 1 x T array, where T is the number of time-steps; in the case of multiple features, you would instead have a “numFeatures” x T array. However, in both those cases we still only have one input to our RNN, in the form of one array which contains all our data information.
One thing to remember is that different features of the input data don't equate to multiple inputs - instead, the data gets fed in N sequences at a time, where each sequence contains information for all the features in one big matrix (which is of size “numFeatures” x T).
The “predictAndUpdateState” function will output a single value, regardless of the number of input features. This depends on how many number of responses the LSTM network is designed to map the input features.
You can check with your “XTest()” dimensions (features x timestep), "numFeatures" and “numResponses” .
Please refer to this example: https://in.mathworks.com/help/deeplearning/ug/sequence-to-sequence-regression-using-deep-learning.html
Hope this helps!
Thanks
