Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

by   Kasun Bandara, et al.

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. Given that 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 nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. 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.


LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

Generating forecasts for time series with multiple seasonal cycles is an...

A multi-series framework for demand forecasts in E-commerce

Sales forecasts are crucial for the E-commerce business. State-of-the-ar...

A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series

A univariate time series with high variability can pose a challenge even...

A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs

Forecasts of product demand are essential for short- and long-term optim...

Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models

Platelet products are both expensive and have very short shelf lives. As...

Towards forecast techniques for business analysts of large commercial data sets using matrix factorization methods

This research article suggests that there are significant benefits in ex...

Please sign up or login with your details

Forgot password? Click here to reset