I'm trying to build a model with Keras to predict the time series of a sensor, based on its type and historic data of sensors of the same type.
The figure below shows 3 time series, generated from 3 sensors of the same type, the green dashed line is the new sensor data and the vertical line is where the data for the new sensor end.
I've tried writing an LSTM network, that returns the hidden state output for each input time step, while the target was the values for each timestamp. Then trying to predict the new time series giving the model a few points of the sensor history data. With no luck :(
So I'm guessing I'm walking on the wrong path. What are the options of predicting a time series with just a few historical samples based on the history of other time series of the same type?
Any help / reference / video would be appericiated