  Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.
