| Форма представления | Тезисы и материалы конференций в зарубежных журналах и сборниках |
| Год публикации | 2025 |
| Язык | английский |
|
Киршин Игорь Александрович, автор
|
| Библиографическое описание на языке оригинала |
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 |
| Аннотация |
Advances in Applied Microeconomics. ICOAE 2024. Springer Proceedings in Business and Economics. Springer |
| Ключевые слова |
Artificial Neural Networks, SARIMA, Medical Time Series Forecasting. |
| Название журнала |
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 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=313463 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Киршин Игорь Александрович |
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/?p_id=313463 |
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 |
Тезисы и материалы конференций в зарубежных журналах и сборниках |
ru_RU |
|
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