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- W2912593433 abstract "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." @default.
- W2912593433 created "2019-02-21" @default.
- W2912593433 creator A5027433743 @default.
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- W2912593433 date "2018-12-01" @default.
- W2912593433 modified "2023-10-16" @default.
- W2912593433 title "Using Real-World Store Data for Foot Traffic Forecasting" @default.
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- W2912593433 doi "https://doi.org/10.1109/bigdata.2018.8622551" @default.
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