Using Real-World Store Data for Foot Traffic Forecasting
| Autor: | Piyush Kumar, Soheila Abrishami |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Foot (prosody)
Computer science business.industry 010103 numerical & computational mathematics 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Task (project management) Work (electrical) 0202 electrical engineering electronic engineering information engineering Wireless 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics Time series business computer |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622551 |
| Popis: | Time series forecasting is a fundamental task in machine learning and data mining. It is an active area of research, especially in applications that have direct impact on the real-world. Foot traffic forecasting is one such application, which has a direct impact on businesses and non-profits alike. In this paper, we propose and compare different prediction models for foot traffic forecasting. Our foot traffic data has been collected from wireless access points deployed at over 65 businesses across the United States, for more than one year. We validate our work by comparing to state-of-the-art time series forecasting approaches. Results show the competitiveness of our proposed method in comparison to our previous work and state-of-the-art procedures for time series forecasting. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|