来自输入数据的 Tensorflow 连续训练

Question

I'm new to tensorflow have a general question: I have a certain amount of training data and want to do a time series prediction. The intervals on my training data is one minute and I want to do a prediction for the following minutes based on the new input data which is provided via a REST API What I don't understand is this: Let's say I train the model with all the data till yesterday and this means I can predict the first values of today by a certain amount. But the new values of today have not been observed by the model that has been build yesterday. How would you solve this problem? Thanks

Answer 1

Djboblo, I assume that you need to predict whole next day values on per minute basis. In that case your options are:

1. recursive prediction, ie using predicted data as input for the next prediction
2. structuring the model to provide you with prediction for the whole next day

If it is just a matter of predicting for a single minute forward and your model is trained on reasonably large amount of data - don't worry, just feed it with the values up to the prediction minute. Periodically you may re-train the model using new data.

Answer 2

What I was looking for is this:

How to use a Keras RNN model to forecast for future dates or events?

to predict stateful events

and after a while use the .fit Method to update the network with new data

See: https://machinelearningmastery.com/update-lstm-networks-training-time-series-forecasting/