  Generating accurate and reliable sales forecasts is crucial in the E-commerce
business. The current state-of-the-art techniques are typically univariate
methods, which produce forecasts considering only the historical sales data of
a single product. However, in a situation where large quantities of related
time series are available, conditioning the forecast of an individual time
series on past behaviour of similar, related time series can be beneficial.
Since the product assortment hierarchy in an E-commerce platform contains large
numbers of related products, in which the sales demand patterns can be
correlated, our attempt is to incorporate this cross-series information in a
unified model. We achieve this by globally training a Long Short-Term Memory
network (LSTM) that exploits the non-linear demand relationships available in
an E-commerce product assortment hierarchy. Aside from the forecasting
framework, we also propose a systematic pre-processing framework to overcome
the challenges in the E-commerce business. We also introduce several product
grouping strategies to supplement the LSTM learning schemes, in situations
where sales patterns in a product portfolio are disparate. We empirically
evaluate the proposed forecasting framework on a real-world online marketplace
dataset from Walmart.com. Our method achieves competitive results on category
level and super-departmental level datasets, outperforming state-of-the-art
techniques.
