Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data?
The dataset that I have is an electricity type data set from a building power meter and I can find I can train a decent NN model with including a lot of weather data and also a lot of one hot encoding dummy variables for time-of-week. (day, hour, month number, etc.)
I am experimenting in Python with the Tensorflow Keras library and I know the default during the training process randomly shuffles the data. Is this a No-No for a time series type problem where the random shuffle will take out the seasonality from the data? (stationary/non-stationary) The results shuffling the data really aren't that bad at a glance but not-randomly shuffling the data the results for MLP NN are poor, like the model doesn't train well.
I know some other times series forecast methods can include ARIMA, LSTM, etc. but I was curious to inquire if MLP can be used for these purposes too? What I ultimately need is a short term forecast method that can incorporate hourly weather forecast (from a web API) to forecast future hourly building electricity. Any tips greatly appreciated.