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arXiv · 1803.03800

ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting

Srayanta Mukherjee·Devashish Shankar·Atin Ghosh·Nilam Tathawadekar·Pramod Kompalli·Sunita Sarawagi·Krishnendu Chaudhury
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Abstract

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.

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Srayanta Mukherjee, Devashish Shankar, Atin Ghosh, Nilam Tathawadekar, Pramod Kompalli, Sunita Sarawagi, Krishnendu Chaudhury. 2018-03-16. ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting. https://arxiv.org/abs/1803.03800

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