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FİNANSAL PERFORMANS TAHMİNİNDE PROPHET MODELİ: İMALAT SEKTÖRÜ UYGULAMASI

Year 2021, , 160 - 166, 31.12.2021
https://doi.org/10.17261/Pressacademia.2021.1470

Abstract

Amaç- Büyük veriyi işleme noktasında şirketler klasik istatistik yöntemlerinden daha hızlı sonuca ulaşabilecekleri yapay zeka yöntemlerini
tercih etmeye başlamışlardır. Bu nedenle son yıllarda yapay zeka yöntemleriyle finansal performans tahmini çalışmaları da giderek
artmaktadır. Farklı zaman serisi modelleri ile finansal performans tahmini çalışmaları olmasına rağmen Facebook Prophet modeli ile yapılan
çalışmaya literatürde rastlanmadığından alandaki boşluğa katkı sağlanması amaçlanmaktadır.
Yöntem- BIST İmalat Sektöründe yer alan 173 şirketin Kamuyu Aydınlatma Platformu veri tabanından elde edilen 2009-2020 yılları arasındaki
verileri, net dönem kar/zararı hedef değişken olarak seçilerek yapay öğrenme modeli Prophet ile Python programı üzerinde çalışarak finansal
performans tahmini yapılmıştır.
Bulgular- Veri setindeki 46 dönem eğitildikten sonra son 2 döneminin tahminine ait MSE değerleri 0,0185 ile 25,0147 arasında, RMSE
değerleri 0,1361 ile 5,0015 ve MAPE değerleri ise 0,1002 ile 4,6634 aralığında ölçülmüştür. Sıfıra yakın değerler yanında sıfırdan uzaklaşan
değerler de bulunmaktadır. Bu da başarılı tahminlerin yanında başarısız tahminlerin de olduğunu göstermektedir.
Sonuç- Prophet yönteminin çok fazla çaba harcamayı gerektirmeden şirketlere zaman kazandıracağı söylenebilir. Doğruluğu ve performansı
daha da geliştirmek için birden fazla modelin güçlü yönlerinden yararlanarak karma bir yapay zeka modeli oluşturmak sonraki çalışmalar için
önerilmektedir.

References

  • Aguilera, H., Albert, C. G., Fernández, N. N., & Kohfahl, C. (2019). Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach. Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 64(12), 1504-1518, DOI: 10.1080/02626667.2019.1651933
  • Akdağ, M., & Bozma, G. (2021). Stok akış modeli ve facebook prophet algoritması ile bitcoin fiyatı tahmini. Uluslararası Ekonomi, İşletme ve Politika Dergisi, 5 (1), 16-30. DOI: 10.29216/ueip.878925
  • Aydemir, O., Ögel, S., & Demirtaş, G. (2012). Hisse senetleri fiyatlarının belirlenmesinde finansal oranların rolü. Yönetim ve Ekonomi Dergisi, 19(2), 277-288.
  • Bayrakdaroğlu, A., Mirgen, C., & Kuyu, E. (2017). Relationship between profitability ratios and stock prices: an empirical analysis on BIST-100.
  • PressAcademia Procedia, 6 (1), 1-10. DOI: 10.17261/Pressacademia.2017.737
  • Cecchetti, G., & Kharroubi, E. (2012). Reassessing the impact of finance on growth. BIS Working Papers. ISSN 1682-7678
  • Chan, W. N. (2020). Time series data mining: comparative study of ARIMA and Prophet methods for forecasting closing prices of Myanmar Stock Exchange. Journal of Computer Applications and Research, 1(1), 75-80.
  • Duarte, D., & Faerman, J. (2019). Comparison of time series prediction of healthcare emergency department indicators with ARIMA and Prophet. Computer Science & Information Technology (CS & IT) Computer Science Conference, 123–33,
  • https://doi.org/10.5121/csit.2019.91810 ...... .............................
  • Gaur, S. (2020). Global forecasting of Covid-19 using ARIMA based FB-Prophet. International Journal of Engineering Applied Sciences and Technology, 5(2), 463-467.
  • Gergin, B., & Kıymetli Şen, İ. (2019). Kurumsal yönetim endeksinde yer almanın bankaların performansına etkisi: Borsa İstanbul'da bir araştırma. Muhasebe Bilim Dünyası Dergisi, 21(4), 956-978. doi:10.31460/mbdd.562606
  • Guo, C., Ge, Q., Jiang, H., Yao, G., Hua, Q. (2020). Maximum power demand prediction using fbprophet with adaptive Kalman filtering. IEEE Access, 8: 19236-19247. https://doi.org/10.1109/ACCESS.2020.2968101
  • Güleryüz, D. & Özden, E. (2020). The prediction of brent crude oil trend using LSTM and facebook Prophet. Avrupa Bilim ve Teknoloji Dergisi, (20), 1-9. DOI: 10.31590/ejosat.759302
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. Australia: OTexts, ISBN 978-0- 9875071-1-2
  • Jha, B. K., & Pande, S. (2021). Time Series Forecasting Model for Supermarket Sales using FB-Prophet. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 547-554, Erode, India. Koç, B., & Yüncü, A. B. (2020). Muhasebe alanında 2004-2018 yılları arasında hazırlanmış lisansüstü tezlerin incelenmesi. Muhasebe Enstitüsü Dergisi (62), 63-75.
  • Law, S. H., & Singh, N. (2014). Does too much finance harm economic growth? Journal of Banking and Finance, 41, 36-44.
  • Madhuri, C., Chinta, M., & Kumar, V. (2020). Stock Market Prediction for Time-series Forecasting using Prophet upon ARIMA. 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 317-321.
  • Mata, A. G. (2020). A Comparison Between LSTM and Facebook Prophet Modles: A Fianacial Forecasting Case Study. Universital Oberta de Catalunya.Journal of Economics, Finance and Accounting – JEFA (2021), 8(4),p.160-166 Yurttabir, SenDOI: 10.17261/Pressacademia.2021.1470 166
  • Phutela, N., Bakshi, A., & Gupta, S. (2020). Forecasting the Stability of COVID-19 on Indian Dataset with Prophet Logistic Growth Model.
  • Research Square, https://doi.org/10.21203/rs.3.rs-32472/v1
  • Samal, K. R., Babu, K. S., Das, S. K., & Acharaya, A. (2019). Time Series based Air Pollution Forecasting using SARIMA and Prophet Model. ITCC 2019, Proceedings of the 2019 International Conference on Information Technology and Computer Communications, 80-85.
  • Schumpeter, J.A. (1983). The Theory of Economic Development, An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle Transaction Publishers, New Brunswick (U.S.A) and London (U.K), ISBN:0-87855-698-2
  • Sevli, O., & Başer, V. G. (2020). Covid-19 sal gınına yönelik zaman serisi verileri ile Prophet model kullanarak makine öğrenmesi temelli vaka tahminlemesi. Avrupa Bilim ve Teknoloji Dergisi, 827-835, DOI: 10.31590/ejosat.766623
  • Taylor, S. J., & Letham, B. (2017). Prophet: Forecasting at Scale Facebook. Eylül 29, 2021 tarihinde https://facebook.github.io/prophet/ adresinden alındı.
  • Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. doi:10.1080/00031305.2017.1380080
  • Uğuz, S. (2019). Makine Öğrenmesi Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü. Ankara: Nobel Akademik Yayıncılık, ISBN:978-605-033-176-9.
  • VeriBilimcisi.com. (2017, Temmuz 14). https://veribilimcisi.com/. Eylül 10, 2021 tarihinde Veri Bilimcisi: https://veribilimcisi.com/2017/07/14/mse-rmse-mae-mape-metrikleri-nedir/ adresinden alındı.
  • Weytjens, H., Lohmann, E., & Kleinsteuber, M. (2019). Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electronic Commerce Research, 21, 371-391, Issue Date (2021), DOI:10.1007/S10660-019-09362-7
  • Žunić, E., Korjenić, K., Hodžić, K., & Đonko, D. (2020). Applicatıon of facebook's Prophet algorithm for successful sales forecastıng based on real-world data. International Journal of Computer Science & Information Technology, 12(2), 23-36, DOI: 10.5121/ijcsit.2020.12203

PROPHET MODEL IN FINANCIAL PERFORMANCE FORECAST: IMPLEMENTATION IN MANUFACTURING SECTOR

Year 2021, , 160 - 166, 31.12.2021
https://doi.org/10.17261/Pressacademia.2021.1470

Abstract

Purpose- Through processing big data, companies have started to prefer artificial intelligence methods that can reach results faster than
classical statistical methods. For this reason, financial performance forecasting studies with artificial intelligence methods have been
increasing in recent years. The purpose of this study is to contribute to the gap in the field of financial performance forecasting with the
Facebook Prophet model which has not been applied before although there are other time series models in the literature.
Methodology- The study employs Facebook Prophet artificial learning model by choosing the net profit/loss as target variable for financial
performance forecasting on Python program with the data of 173 companies in the BIST Manufacturing Sector between 2009 and 2020,
obtained from the Public Disclosure Platform.
Findings- After training the 46 periods of the data set, performance matrices of MSE, RMSE and MAPE were measured for the last 2 periods
as MSE values between 0.0185 and 25.0147, RMSE values between 0.1361 and 5.0015 and MAPE values between 0.1002 and 4.6634. In
addition to values close to zero, there are also values that move away from zero. The analysis reveals that besides successful predictions,
there are also unsuccessful predictions.
Conclusion- It may be concluded that the Facebook Prophet method will save companies time without requiring much effort. To further
improve accuracy and performance, creating a mixed artificial intelligence model by leveraging the strengths of multiple models is
recommended for further studies.

References

  • Aguilera, H., Albert, C. G., Fernández, N. N., & Kohfahl, C. (2019). Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach. Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 64(12), 1504-1518, DOI: 10.1080/02626667.2019.1651933
  • Akdağ, M., & Bozma, G. (2021). Stok akış modeli ve facebook prophet algoritması ile bitcoin fiyatı tahmini. Uluslararası Ekonomi, İşletme ve Politika Dergisi, 5 (1), 16-30. DOI: 10.29216/ueip.878925
  • Aydemir, O., Ögel, S., & Demirtaş, G. (2012). Hisse senetleri fiyatlarının belirlenmesinde finansal oranların rolü. Yönetim ve Ekonomi Dergisi, 19(2), 277-288.
  • Bayrakdaroğlu, A., Mirgen, C., & Kuyu, E. (2017). Relationship between profitability ratios and stock prices: an empirical analysis on BIST-100.
  • PressAcademia Procedia, 6 (1), 1-10. DOI: 10.17261/Pressacademia.2017.737
  • Cecchetti, G., & Kharroubi, E. (2012). Reassessing the impact of finance on growth. BIS Working Papers. ISSN 1682-7678
  • Chan, W. N. (2020). Time series data mining: comparative study of ARIMA and Prophet methods for forecasting closing prices of Myanmar Stock Exchange. Journal of Computer Applications and Research, 1(1), 75-80.
  • Duarte, D., & Faerman, J. (2019). Comparison of time series prediction of healthcare emergency department indicators with ARIMA and Prophet. Computer Science & Information Technology (CS & IT) Computer Science Conference, 123–33,
  • https://doi.org/10.5121/csit.2019.91810 ...... .............................
  • Gaur, S. (2020). Global forecasting of Covid-19 using ARIMA based FB-Prophet. International Journal of Engineering Applied Sciences and Technology, 5(2), 463-467.
  • Gergin, B., & Kıymetli Şen, İ. (2019). Kurumsal yönetim endeksinde yer almanın bankaların performansına etkisi: Borsa İstanbul'da bir araştırma. Muhasebe Bilim Dünyası Dergisi, 21(4), 956-978. doi:10.31460/mbdd.562606
  • Guo, C., Ge, Q., Jiang, H., Yao, G., Hua, Q. (2020). Maximum power demand prediction using fbprophet with adaptive Kalman filtering. IEEE Access, 8: 19236-19247. https://doi.org/10.1109/ACCESS.2020.2968101
  • Güleryüz, D. & Özden, E. (2020). The prediction of brent crude oil trend using LSTM and facebook Prophet. Avrupa Bilim ve Teknoloji Dergisi, (20), 1-9. DOI: 10.31590/ejosat.759302
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. Australia: OTexts, ISBN 978-0- 9875071-1-2
  • Jha, B. K., & Pande, S. (2021). Time Series Forecasting Model for Supermarket Sales using FB-Prophet. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 547-554, Erode, India. Koç, B., & Yüncü, A. B. (2020). Muhasebe alanında 2004-2018 yılları arasında hazırlanmış lisansüstü tezlerin incelenmesi. Muhasebe Enstitüsü Dergisi (62), 63-75.
  • Law, S. H., & Singh, N. (2014). Does too much finance harm economic growth? Journal of Banking and Finance, 41, 36-44.
  • Madhuri, C., Chinta, M., & Kumar, V. (2020). Stock Market Prediction for Time-series Forecasting using Prophet upon ARIMA. 7th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 317-321.
  • Mata, A. G. (2020). A Comparison Between LSTM and Facebook Prophet Modles: A Fianacial Forecasting Case Study. Universital Oberta de Catalunya.Journal of Economics, Finance and Accounting – JEFA (2021), 8(4),p.160-166 Yurttabir, SenDOI: 10.17261/Pressacademia.2021.1470 166
  • Phutela, N., Bakshi, A., & Gupta, S. (2020). Forecasting the Stability of COVID-19 on Indian Dataset with Prophet Logistic Growth Model.
  • Research Square, https://doi.org/10.21203/rs.3.rs-32472/v1
  • Samal, K. R., Babu, K. S., Das, S. K., & Acharaya, A. (2019). Time Series based Air Pollution Forecasting using SARIMA and Prophet Model. ITCC 2019, Proceedings of the 2019 International Conference on Information Technology and Computer Communications, 80-85.
  • Schumpeter, J.A. (1983). The Theory of Economic Development, An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle Transaction Publishers, New Brunswick (U.S.A) and London (U.K), ISBN:0-87855-698-2
  • Sevli, O., & Başer, V. G. (2020). Covid-19 sal gınına yönelik zaman serisi verileri ile Prophet model kullanarak makine öğrenmesi temelli vaka tahminlemesi. Avrupa Bilim ve Teknoloji Dergisi, 827-835, DOI: 10.31590/ejosat.766623
  • Taylor, S. J., & Letham, B. (2017). Prophet: Forecasting at Scale Facebook. Eylül 29, 2021 tarihinde https://facebook.github.io/prophet/ adresinden alındı.
  • Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. doi:10.1080/00031305.2017.1380080
  • Uğuz, S. (2019). Makine Öğrenmesi Teorik Yönleri ve Python Uygulamaları ile Bir Yapay Zeka Ekolü. Ankara: Nobel Akademik Yayıncılık, ISBN:978-605-033-176-9.
  • VeriBilimcisi.com. (2017, Temmuz 14). https://veribilimcisi.com/. Eylül 10, 2021 tarihinde Veri Bilimcisi: https://veribilimcisi.com/2017/07/14/mse-rmse-mae-mape-metrikleri-nedir/ adresinden alındı.
  • Weytjens, H., Lohmann, E., & Kleinsteuber, M. (2019). Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electronic Commerce Research, 21, 371-391, Issue Date (2021), DOI:10.1007/S10660-019-09362-7
  • Žunić, E., Korjenić, K., Hodžić, K., & Đonko, D. (2020). Applicatıon of facebook's Prophet algorithm for successful sales forecastıng based on real-world data. International Journal of Computer Science & Information Technology, 12(2), 23-36, DOI: 10.5121/ijcsit.2020.12203
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Economics, Finance, Business Administration
Journal Section Articles
Authors

Ali Yurttabir This is me 0000-0001-8689-3476

İlker Kıymetli Şen

Publication Date December 31, 2021
Published in Issue Year 2021

Cite

APA Yurttabir, A., & Kıymetli Şen, İ. (2021). FİNANSAL PERFORMANS TAHMİNİNDE PROPHET MODELİ: İMALAT SEKTÖRÜ UYGULAMASI. Journal of Economics Finance and Accounting, 8(4), 160-166. https://doi.org/10.17261/Pressacademia.2021.1470

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