KFU. Card publication. Clinic Daily Outpatient Visits Forecasting Using a Combination Method Based on Neural Networks and SARIMA Model

KAZAN
FEDERAL UNIVERSITY

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CLINIC DAILY OUTPATIENT VISITS FORECASTING USING A COMBINATION METHOD BASED ON NEURAL NETWORKS AND SARIMA MODEL

Form of presentation

Conference proceedings in international journals and collections

Year of publication

2025

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Authors, employees of KFU

Kirshin Igor Aleksandrovich, author

Bibliographic description in the original language

1. Kirshin, I., Kirshin, D., Minnullin, R. (2025). Clinic Daily Outpatient Visits Forecasting Using a Combination Method Based on Neural Networks and SARIMA Model. In: Tsounis, N., Vlachvei, A. (eds) Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer, Cham. pp. 109-127. https://doi.org/10.1007/978-3-031-76654-1_6

Annotation

Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer

Keywords

Artificial Neural Networks, SARIMA, Medical Time Series Forecasting.

The name of the journal

Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer

URL

https://doi.org/10.1007/978-3-031-76654-1_6

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=313461&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Kirshin Igor Aleksandrovich	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	1. Kirshin, I., Kirshin, D., Minnullin, R. (2025). Clinic Daily Outpatient Visits Forecasting Using a Combination Method Based on Neural Networks and SARIMA Model. In: Tsounis, N., Vlachvei, A. (eds) Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer, Cham. pp. 109-127. https://doi.org/10.1007/978-3-031-76654-1_6	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=313461&p_lang=2	ru_RU
dc.description.abstract	Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer	ru_RU
dc.description.abstract	Research hypothesis: Time series modeling can provide accurate daily outpatient visits forecasting of clinic. A combined approach of using two forecasting models: Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) stochastic model to approximate nonlinear and linear relationships, respectively, increases forecasting accuracy by taking into account the specifics of outpatient visits on weekdays and weekends, or the so-called the day week effect. Method: Modeling the forecast of stochastic time series. Dataset: The original single time series consists of 787 cases - the number of daily outpatient visits (persons) to the Kazan clinic (Russia) in the time periods: 01.01.2022 - 02.27.2024. Results: The validity of the hypothesis is confirmed. By combining the complementary capabilities of Artificial Neural Networks and the SARIMA model, there was an improvement in the accuracy of forecasting daily outpatient visits in the short term. The weekend forecast results demonstrated that Mean Absolute Percentage Error is significantly reduced when using neural networks. The obtained Mean Absolute Percentage Error estimates for working days of the week showed the best results when applying the SARIMA model. Conclusion: Artificial Neural Networks time series forecasting models in combination with the SARIMA model allow better to specify of the time series and more accurate modeling of the short-term forecast.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Artificial Neural Networks	ru_RU
dc.subject	SARIMA	ru_RU
dc.subject	Medical Time Series Forecasting.	ru_RU
dc.title	Clinic Daily Outpatient Visits Forecasting Using a Combination Method Based on Neural Networks and SARIMA Model	ru_RU
dc.type	Conference proceedings in international journals and collections	ru_RU