I have a task where I want to forecast daily observations for 1 year or 2 years in advance at multiple locations--so 365 or 730 days in advance. I actually have a pretty good dataset, meaning daily observations going back 40 years at those sites. So I have a lot of data to train with. I am working with climate and geologic data, so the time series is strongly cyclical or periodic with some random variation.
I was trying to use a Sequence-to-Sequence LSTM network with attention to start with. I have 365 days input and trying to predict 365 days output. I am just starting to collect the results so far. But I was wondering if there are any specific models designed for long-range time-series forecasting? I thought sequence to sequence models still only worked well for say 20-30 day output, but I was not sure if there has been any developments on that front.