Description
Title: Time Series Forecasting Using Machine Learning in the Banking Sector
Abstract: Time series forecasting is a highly contentious issue. Machine learning algorithms have been widely used in this field recently. In order to address optimization issues in the banking industry, this paper outlines a methodical approach to developing a machine learning predictive model. The application of such techniques in this particular field is the subject of a literature review. A general scenario for forecasting various non-stationary time series in automatic mode was created as a direct outcome of the research as described. Consideration is given to the developed scenario for resolving particular banking tasks to increase business effectiveness, including optimizing ATM demand and predicting the workload on the call center and cash center. This article describes a machine learning approach to economics that can produce reliable, repeatable results and be applied to other tasks that are similar. The article’s methodology was tested on three cases and demonstrated the ability to produce models that are at least three percentage points more accurate than comparable predictive models described in the literature. Due to the numerous links to systematic literature reviews on the subject, this article will be beneficial to specialists working on the issue of forecasting economic time series as well as students and researchers.
Keywords: machine learning; artificial neural networks; data mining; ATMs; time series forecasting; load forecasting; service optimization
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Economics
Writer Experience: 20+ Years
Plagiarism Report: Turnitin Plagiarism Report will be less than 10%
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