App Download Forecasting: An Evolutionary Hierarchical Competition Approach

App Download Forecasting: An Evolutionary Hierarchical Competition Approach

Yingzi Wang, Nicholas Jing Yuan, Yu Sun, Chuan Qin, Xing Xie

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2978-2984. https://doi.org/10.24963/ijcai.2017/415

Product sales forecasting enables comprehensive understanding of products' future development, making it of particular interest for companies to improve their business, for investors to measure the values of firms, and for users to capture the trends of a market. Recent studies show that the complex competition interactions among products directly influence products' future development. However, most existing approaches fail to model the evolutionary competition among products and lack the capability to organically reflect multi-level competition analysis in sales forecasting. To address these problems, we propose the Evolutionary Hierarchical Competition Model (EHCM), which effectively considers the time-evolving multi-level competition among products. The EHCM model systematically integrates hierarchical competition analysis with multi-scale time series forecasting. Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities.
Keywords:
Machine Learning: Data Mining
Machine Learning: Time-series/Data Streams