RNN/LSTM timeseries, with fixed attributes per run
I have a multivariate time series of weather date: temperature, humidity and wind strength ($x_{c,t},y_{c,t},z_{c,t}$ respectively). I have this data for a dozen different cities ($c\in {c_1,c_2,...,c_{12}}$).
I also know the values of certain fixed attributes for each city. For example, altitude ($A$), latitude $(L)$ and distance from ocean ($D$) are fixed for each city (i.e. they are time independent). Let $p_c=(A_c,L_c,D_c)$ be this fixed parameter vector for city $c$.
I have built a LSTM in Keras (based on this post) to predict the time series from some initial starting point, but this does not make use of $p_c$ (it just looks at the time series values). My question is:
Can the fixed parameter vector $p_c$ be taken into account when designing/training my network?
The purpose of this is essentially: (1) train a LSTM on all data from all cities, then (2) forecast the weather time series for a new city, with known $A_{new},L_{new},D_{new}$ values (but no other data - i.e. no weather history for this city).
(A structure different from LSTM is fine, if that's more suited.)
Topic lstm keras rnn neural-network time-series
Category Data Science