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Pattern Recognition and Image Analysis

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01.12.2022 | APPLIED PROBLEMS

ATM Cash Flow Prediction Using Local and Global Model Approaches in Cash Management Optimization

verfasst von: A. Riabykh, I. Suleimanov, D. Surzhko, M. Konovalikhin, V. Ryazanov

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 4/2022

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Cash management optimization is one of the most essential tasks for any bank, because it helps save a significant amount of money by reducing the cost of ATMs funding and encashment. This paper focuses on forecasting customer cash demand, which is one of the key components of the optimization system. Furthermore, for the first time, our research touches on the problem of nonstationarity, which is typical for real-world ATM data, and proposes a data preprocessing pipeline to tackle it. We proposed new forecasting methods in the paradigms of local and global models, proved their superiority over classical approaches to forecasting time series and approaches used specifically for the cash demand forecasting problem.

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In the local model approach, one model is built per one time series, in the global model one model per multiple time series.

WPE distribution parameters are secondary and needed only for a deeper analysis of results and assessing the effect of data preprocessing. The best model is selected based on the WAPE metric.

We call time series from the final preprocessed dataset to which we select history as the current series

Cross-validation is done in an expanding window that functions two windows: the training window and the forecasting window. The model trains on the first window and predicts for the second. Then these windows are iteratively shifted forward along with the time series, and at the end, the metric WAPE (defined in Section 3.1) is calculated and averaged over all forecasting windows.

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Titel: ATM Cash Flow Prediction Using Local and Global Model Approaches in Cash Management Optimization
verfasst von: A. Riabykh
I. Suleimanov
D. Surzhko
M. Konovalikhin
V. Ryazanov
Publikationsdatum: 01.12.2022
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 4/2022
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661822040113

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