On the prediction of dispenser status in ATM using gradient boosting method

Shcherbitsky, V. V.; Panachev, A. A.; Medvedeva, Marina A.; Казакова, Елена Ивановна

doi:10.1063/1.5137948

Cited by 2 publications

(1 citation statement)

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“…Also, classical and deep learning methods were applied to this problem, the performance comparison of which are given in Vangala and Vadlamani (2020) and Hasheminejad and Reisjafari (2017). However, an analysis of the literature shows that classical machine learning methods such as regression analysis (Rajwani et al 2017), support vector machines (Jadwal et al 2018), dynamic programming (Ozer et al 2019), ARIMA (Khanarsa and Sinapiromsaran 2017), and gradient boosting (Shcherbitsky et al 2019) are used much more often.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector

2021

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The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A literature analysis on applying such methods in this particular area is presented. As a direct result of the described research, a universal scenario for forecasting various non-stationary time series in automatic mode was developed. The developed scenario for solving specific banking tasks to improve business efficiency, including optimizing demand for ATMs, forecasting the load on the call center and cash center, is considered. A machine learning methodology in economics that can yield robust and reproducible results and can be reused in solving other similar tasks is described. The methodology described in the article was tested on three cases and showed the ability to generate models that are superior in accuracy to similar predictive models described in the literature by at least three percentage points. This article will be helpful to specialists dealing with the problem of forecasting economic time series and students and researchers due to a large number of links to systematic literature reviews on this topic.

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