I am trying to model a health outcome as a function of climate variables. I have many observations of health outcomes at different times and locations (but NOT a sequence at one location). For each health observation, I have the temperature and rainfall for every month before the health observation up to 24 months. Lets say I have 25,000 observations.
So my outcome is the shape
(25000, ) and each is a rate from 0 to 1.
My predictors are the shape
(25000, 24, 2), i.e., 24000 observations, over 24 months, for two features.
In trying to get a sense of what neural network architecture I should use, I keep coming across LSTMs. However, most applications seem to use a times series of some training variable (say, stocks prices, rainfall levels, or a sequence of text) to learn trends and the make predictions of that same variable. But I want use weather data to predict a different variable, and my outcome is a single value (not a sequence).
Is LSTM appropriate for this application? If not, what type of architecture should I look into here?