Spatio-Temporal Multi-graph Networks for Demand Forecasting in Online Marketplaces

Ankit Gandhi, Sivaramakrishnan Kaveri, Vineet Chaoji

Abstract:

Demand forecasting is fundamental to successful inventory
planning and optimisation of logistics costs for online marketplaces such
as Amazon. Millions of products and thousands of sellers are competing
against each other in an online marketplace. In this paper, we propose
a framework to forecast demand for a product from a particular seller
(referred as offer/seller-product demand in the paper). Inventory planning
and placements based on these forecasts help sellers in lowering fulfilment costs, improving instock availability and increasing shorter delivery
promises to the customers. Most of the recent forecasting approaches
in the literature are one-dimensional, i.e, during prediction, the future
forecast mainly depends on the offer i.e. its historical sales and features.
These approaches don’t consider the effect of other offers and hence, fail
to capture the correlations across different sellers and products seen in
situations like, (i) competition between sellers offering similar products,
(ii) effect of a seller going out of stock for the product on competing
seller, (iii) launch of new competing products/offers and (iv) cold start
offers or offers with very limited historical sales data. In this paper, we
propose a general demand forecasting framework for multivariate correlated time series.

The proposed technique models the homogeneous and
heterogeneous correlations between sellers and products across different
time series using graph neural networks (GNN) and uses state-of-the-art
forecasting models based upon LSTMs and TCNs for modelling individual
time series. We have experimented with various GNN architectures such
as GCNs, GraphSAGE and GATs for modelling the correlations. We
applied the framework to forecast the future demand of products, sold
on Amazon, for each seller and we show that it performs ∼16% better
than state-of-the-art forecasting approaches.