  Current time-series forecasting problems use short-term weather attributes as
exogenous inputs. However, in specific time-series forecasting solutions (e.g.,
demand prediction in the supply chain), seasonal climate predictions are
crucial to improve its resilience. Representing mid to long-term seasonal
climate forecasts is challenging as seasonal climate predictions are uncertain,
and encoding spatio-temporal relationship of climate forecasts with demand is
complex.
  We propose a novel modeling framework that efficiently encodes seasonal
climate predictions to provide robust and reliable time-series forecasting for
supply chain functions. The encoding framework enables effective learning of
latent representations -- be it uncertain seasonal climate prediction or other
time-series data (e.g., buyer patterns) -- via a modular neural network
architecture. Our extensive experiments indicate that learning such
representations to model seasonal climate forecast results in an error
reduction of approximately 13\% to 17\% across multiple real-world data sets
compared to existing demand forecasting methods.
