I am currently trying to build an LSTM model by using multivariate inputs, but I don't understand what exact output I am predicting.
I am currently using 5 features in the data as input data:
X_data = data[['Time', 'Avg CPU Load', 'F1', 'F2', 'F3']]
My main goal is to predict 'Avg CPU load' by using the other features as input data, but I am not sure if I am doing that.
In these features, I have made 'Time' as an index in the data frame. Then I have used 60 timesteps in the below code.
new_X_train= new_Y_train= for i in range(len(X_data)-timesteps-1): t= for j in range(0,timesteps): t.append(X_data[[(i+j)], :]) new_X_train.append(t) new_Y_train.append(X_data[i+ timesteps,1])
The variables 'new_X_train' and 'new_Y_train' have the inputs data that have to be inserted into the model. Then I define my model which is.
sq = Sequential() sq.add(LSTM(units = 100, return_sequences = True, input_shape = (None, 4))) sq.add(Dropout(0.2)) sq.add(LSTM(units = 100,return_sequences = True)) sq.add(Dropout(0.2)) sq.add(LSTM(units = 100,return_sequences = True)) sq.add(Dropout(0.2)) sq.add(LSTM(units = 100)) sq.add(Dropout(0.2)) sq.add(Dense(units=1)) # only one output sq.compile(optimizer = 'adam', loss = 'mean_squared_error') sq.fit(new_X_train, new_Y_train, epochs = 5, batch_size = 20)
Once the model is trained, I load the test data and implement 60 timesteps. I.E: I do the same for what I have done for the training data, and then I use the
X_test data to predict the values.
predicted = sq.predict(X_test)
Now I am not sure what output the above code is predicting, like what feature is it trying to predict?
And how to make my model predict the 'Avg CPU load' feature?