A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector
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 lite...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Economies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7099/9/4/205 |
| _version_ | 1797505311251103744 |
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| author | Olga Gorodetskaya Yana Gobareva Mikhail Koroteev |
| author_facet | Olga Gorodetskaya Yana Gobareva Mikhail Koroteev |
| author_sort | Olga Gorodetskaya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:16:48Z |
| format | Article |
| id | doaj.art-3e0278fd902d4f5c828dfc22bccb5b9c |
| institution | Directory Open Access Journal |
| issn | 2227-7099 |
| language | English |
| last_indexed | 2024-03-10T04:16:48Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Economies |
| spelling | doaj.art-3e0278fd902d4f5c828dfc22bccb5b9c2023-11-23T07:59:24ZengMDPI AGEconomies2227-70992021-12-019420510.3390/economies9040205A Machine Learning Pipeline for Forecasting Time Series in the Banking SectorOlga Gorodetskaya0Yana Gobareva1Mikhail Koroteev2Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125167 Moscow, RussiaDepartment of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125167 Moscow, RussiaDepartment of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125167 Moscow, RussiaThe 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.https://www.mdpi.com/2227-7099/9/4/205machine learningartificial neural networksdata miningATMstime series forecastingload forecasting |
| spellingShingle | Olga Gorodetskaya Yana Gobareva Mikhail Koroteev A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector Economies machine learning artificial neural networks data mining ATMs time series forecasting load forecasting |
| title | A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector |
| title_full | A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector |
| title_fullStr | A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector |
| title_full_unstemmed | A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector |
| title_short | A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector |
| title_sort | machine learning pipeline for forecasting time series in the banking sector |
| topic | machine learning artificial neural networks data mining ATMs time series forecasting load forecasting |
| url | https://www.mdpi.com/2227-7099/9/4/205 |
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