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

Name: Comparison of computational intelligence models on forecasting automated teller machine cash demands
Author: Alkaya, A. F., Gultekin, O. G., Danaci, E., Duman, Ekrem

İsim	Comparison of computational intelligence models on forecasting automated teller machine cash demands
Yazar	Alkaya, A. F., Gultekin, O. G., Danaci, E., Duman, Ekrem
Basım Tarihi:	2020
Basım Yeri	- Old City Publishing
Konu	Time series, Forecasting, Regression, Neural networks, Automated teller machine cash demands, Fuzzy time series, Computational intelligence
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1542-3980
Kayıt Numarası	1bc5d0cd-54fa-429a-8157-ec4bf6e8f4f7
Lokasyon	Industrial Engineering
Tarih	2020
Örnek Metin	We take up the problem of forecasting the amount of money to be withdrawn from automated teller machines (ATM).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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Comparison of computational intelligence models on forecasting automated teller machine cash demands

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

Basım Tarihi 2020

Basım Yeri - Old City Publishing

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1542-3980

Kayıt Numarası 1bc5d0cd-54fa-429a-8157-ec4bf6e8f4f7

Lokasyon Industrial Engineering

Tarih 2020

Örnek Metin 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