Predicting shopper volume using ARIMA on public Wi-Fi signals

Ian K.T. Tan; Ooi Boon Yaik; Ooi Boon Sheng

Predicting shopper volume using ARIMA on public Wi-Fi signals

Ian K.T. Tan, Ooi Boon Yaik, Ooi Boon Sheng

Research output: Contribution to journal › Article › Research › peer-review

Abstract

Shopping malls are being built at a rapid pace in many South East Asian countries and it has become competitive to attract and maintain shoppers. Being able to know the volume of shoppers and predicting the volume will greatly benefit mall management. In this paper, we present shopper volume monitoring using Wi-Fi signal detectors and use the data obtained from it to derive an Auto-Regressive Integrated Moving Average (ARIMA) model for shopper volume prediction.

Original language	English
Pages (from-to)	3295-3300
Number of pages	6
Journal	Information
Volume	19
Issue number	8(A)
Publication status	Published - Aug 2016
Externally published	Yes

Keywords

Arima
Prediction
Shopper volume
Shopping mall
Wi-fi

Cite this

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title = "Predicting shopper volume using ARIMA on public Wi-Fi signals",

abstract = "Shopping malls are being built at a rapid pace in many South East Asian countries and it has become competitive to attract and maintain shoppers. Being able to know the volume of shoppers and predicting the volume will greatly benefit mall management. In this paper, we present shopper volume monitoring using Wi-Fi signal detectors and use the data obtained from it to derive an Auto-Regressive Integrated Moving Average (ARIMA) model for shopper volume prediction.",

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AU - Yaik, Ooi Boon

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N2 - Shopping malls are being built at a rapid pace in many South East Asian countries and it has become competitive to attract and maintain shoppers. Being able to know the volume of shoppers and predicting the volume will greatly benefit mall management. In this paper, we present shopper volume monitoring using Wi-Fi signal detectors and use the data obtained from it to derive an Auto-Regressive Integrated Moving Average (ARIMA) model for shopper volume prediction.

AB - Shopping malls are being built at a rapid pace in many South East Asian countries and it has become competitive to attract and maintain shoppers. Being able to know the volume of shoppers and predicting the volume will greatly benefit mall management. In this paper, we present shopper volume monitoring using Wi-Fi signal detectors and use the data obtained from it to derive an Auto-Regressive Integrated Moving Average (ARIMA) model for shopper volume prediction.

KW - Arima

KW - Prediction

KW - Shopper volume

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KW - Wi-fi

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Predicting shopper volume using ARIMA on public Wi-Fi signals

Abstract

Keywords

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