Towards optimal ATM cash replenishment using time series analysis

Article type: Research Article

Authors: Rafi, Muhammad^{; *} | Wahab, Mohammad Taha | Khan, Muhammad Bilal | Raza, Hani

Affiliations: Computer Science Department, National University of Computer & Emerging Sciences, Shah Latif Town, Karachi, Pakistan

Correspondence: [*] Corresponding author. Muhammad Rafi, Computer Science Department, National University of Computer & Emerging Sciences, Shah Latif Town, Karachi, Pakistan. E-mail: [email protected].

Abstract: Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

Keywords: Time series analysis, amazon deep ar, recurrent neural network, ATM cash prediction

DOI: 10.3233/JIFS-201953

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5915-5927, 2021