How to train LSTM with daily timeseries?
Do you mean you mave multiple sensors? Ir one single sensor that record one measurement every day?

In short, you will need to simply match the dimensions of your data to the LSTM. To do that, you design your LSTM to consume tensors of the correct shape.

Let's make the following assumptions:


You have 2 sensors
Each sensor records one measurement per hour (so 24 per day)
You want to consider blocks of 5 consecutive hours to predict the 6th hour


Your LSTM shape would have the following input_shape shape:

(5, 2)


You can pass any number of these sample blocks as a batch, using a batch_size parameter.

So it summarises to this format:

(batch_size, num_time_steps, num_features)


This is a brief introduction that hopefully generalises the idea for you. I would suggest looking at more examples to start getting a feeling for the required dimensions and the terminology that people generally use.


Have a look at this answer, where I explained relevant points for a similar question.
If you have multiple sensors, you need a multivariate LSTM - check out this answer
Another Great Source that explains LSTM use in TimeSeries Jason Brownlee - LSTM TimeSeries Prediction in Keras