Sales Forecast in E-commerce using Convolutional Neural Network

TL;DR

This paper introduces a CNN-based method for automatic feature extraction from raw e-commerce log data to improve sales forecasting accuracy, reducing manual effort and leveraging large real-world datasets.

Contribution

The paper presents a novel CNN approach that automatically learns features from structured log data for sales prediction, overcoming manual feature engineering limitations.

Findings

Validated on CaiNiao.com dataset

Achieved improved forecast accuracy

Demonstrated effectiveness of CNN features

Abstract

Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors including promotion activities, price changes, and user preferences etc. Traditional sales forecast techniques mainly rely on historical sales data to predict future sales and their accuracies are limited. Some more recent learning-based methods capture more information in the model to improve the forecast accuracy. However, these methods require case-by-case manual feature engineering for specific commercial scenarios, which is usually a difficult, time-consuming task and requires expert knowledge. To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN). When fed with raw log data, our approach can automatically extract effective features from that and then forecast sales using those extracted features. We test our method on a large real-world dataset from CaiNiao.com and the experimental results validate the effectiveness of our method.