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Comparison of computational intelligence models on forecasting automated teller machine cash demands

Title	Comparison of computational intelligence models on forecasting automated teller machine cash demands
Author	Alkaya, A. F., Gultekin, O. G., Danaci, E., Duman, Ekrem
Publication Date:	2020
Publication Place	- Old City Publishing
Subject	Time series, Forecasting, Regression, Neural networks, Automated teller machine cash demands, Fuzzy time series, Computational intelligence
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1542-3980
Record ID	1bc5d0cd-54fa-429a-8157-ec4bf6e8f4f7
Library Location	Industrial Engineering
Date	2020
Sample Text	We take up the problem of forecasting the amount of money to be withdrawn from automated teller machines (ATM). We compare the performances of eleven different algorithms from four different research areas on two different datasets. The exploited algorithms are fuzzy time series, multiple linear regression, artificial neural network, autoregressive integrated moving average, gaussian process regression, support vector regression, long-short term memory, simultaneous perturbation stochastic approximation, migrating birds optimization, differential evolution, and particle swarm optimization. The first dataset is very volatile and is obtained from a Turkish bank whereas the more stationary second dataset is obtained from a UK bank which was used in competitions previously. We use mean absolute deviation (MAD) to compare the algorithms since it provides a universal comparison ability independent of the magnitude of the data. The results show that support vector regression (SVR) performs the best on both data sets with a very short run time.
Cilt	35

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Özyeğin University

Comparison of computational intelligence models on forecasting automated teller machine cash demands

Author Alkaya, A. F., Gultekin, O. G., Danaci, E., Duman, Ekrem

Publication Date 2020

Publication Place - Old City Publishing

Subject Time series, Forecasting, Regression, Neural networks, Automated teller machine cash demands, Fuzzy time series, Computational intelligence

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1542-3980

Record ID 1bc5d0cd-54fa-429a-8157-ec4bf6e8f4f7

Library Location Industrial Engineering

Date 2020

Sample Text We take up the problem of forecasting the amount of money to be withdrawn from automated teller machines (ATM). We compare the performances of eleven different algorithms from four different research areas on two different datasets. The exploited algorithms are fuzzy time series, multiple linear regression, artificial neural network, autoregressive integrated moving average, gaussian process regression, support vector regression, long-short term memory, simultaneous perturbation stochastic approximation, migrating birds optimization, differential evolution, and particle swarm optimization. The first dataset is very volatile and is obtained from a Turkish bank whereas the more stationary second dataset is obtained from a UK bank which was used in competitions previously. We use mean absolute deviation (MAD) to compare the algorithms since it provides a universal comparison ability independent of the magnitude of the data. The results show that support vector regression (SVR) performs the best on both data sets with a very short run time.

Cilt 35