  Multi-horizon probabilistic time series forecasting has wide applicability to
real-world tasks such as demand forecasting. Recent work in neural time-series
forecasting mainly focus on the use of Seq2Seq architectures. For example,
MQTransformer - an improvement of MQCNN - has shown the state-of-the-art
performance in probabilistic demand forecasting. In this paper, we consider
incorporating cross-entity information to enhance model performance by adding a
cross-entity attention mechanism along with a retrieval mechanism to select
which entities to attend over. We demonstrate how our new neural architecture,
MQRetNN, leverages the encoded contexts from a pretrained baseline model on the
entire population to improve forecasting accuracy. Using MQCNN as the baseline
model (due to computational constraints, we do not use MQTransformer), we first
show on a small demand forecasting dataset that it is possible to achieve ~3%
improvement in test loss by adding a cross-entity attention mechanism where
each entity attends to all others in the population. We then evaluate the model
with our proposed retrieval methods - as a means of approximating an attention
over a large population - on a large-scale demand forecasting application with
over 2 million products and observe ~1% performance gain over the MQCNN
baseline.
