پیش‌بینی تعهدهای آتی شرکت‌های بیمه با استفاده از مدل حافظۀ بلندمدت ـ کوتاه‌مدت

References

Abbasian, E., Ebrahimi, M. & Farzanegan, E. (2015). Optimal design of securitization in a principal-agent relationship based on Bayesian inference for moral hazard. Financial Research Journal, 17(1), 123-140. doi: 10.22059/jfr.2015.50708 (in Persian)

Ahmadi, S., & Nabizadeh, A. (2023). Using Convolutional Long Short-Term Memory Model for Flood Prediction in Golestan Province, Iran. Journal of Remote Sensing and Geoinformation Research, 1(2), 239-246. doi: 10.22061/jrsgr.2023.2021 (in Persian)

Araujo, J., Patnam, M., Popescu, A., Valencia, F., & Yao, W. (2024). Effects of macroprudential policy: Evidence from over 6000 estimates. Journal of Banking & Finance, 169, 107273.

Avanzi, B., Wong, B. & Yang, X. (2016). A micro-level claim count model with overdispersion and reporting delays. Insurance: Mathematics and Economics, 71, 1-14.

Baghbabi, G. & Eskandari, F. (2017). Estimation of Input & Output Cash of Tejarat Branches in order to Calculate Branches’ Required Cash Via Multivariate Bayesian Clustering Analysis and the Implementation in Neural Network. Financial Research Journal, 19(1), 41-60. doi: 10.22059/jfr.2017.60152 (in Persian)

Balona, C., & Richman, R. (2020). The actuary and IBNR techniques: A machine learning approach. Available at SSRN 3697256.

Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., & Maass, W. (2018). Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in neural information processing systems, 31.

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).

Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2020). Machine learning in P&C insurance: A review for pricing and reserving. Risks, 9(1), 4.

Bolancé, C. & Vernic, R. (2020). Frequency and severity dependence in the collective risk model: an approach based on Sarmanov distribution. Mathematics, 8(9), 1400.

Brownlee, J. (2018). How to develop LSTM models for time series forecasting. Machine learning mastery, 14.

Brownlee, J. (2019). How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks. Deep Learning for Time Series.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Model Development Discussion, 7(3),1247-1250.

Chen, G. (2018). A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation. arXiv:1801.01078, 1-10.

Cheng, G., Peddinti, V., Povey, D., Manohar, V., Khudanpur, S., & Yan, Y. (2017, August). An Exploration of Dropout with LSTMs. In Interspeech (pp. 1586-1590).

Crevecoeur, J., Antonio, K. & Verbelen, R. (2019). Modeling the number of hidden events subject to observation delay. European Journal of Operational Research, 277(3), 930-944.

De Felice, M., & Moriconi, F. (2019). Claim watching and individual claims reserving using classification and regression trees. Risks, 7(4), 102.

Ding, G. & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307-1317.

Elsworth, S., & Güttel, S. (2020). Time series forecasting using LSTM networks: A symbolic approach. arXiv preprint arXiv:2003.05672.

Embrechts, P., Mao, T., Wang, Q., & Wang, R. (2021). Bayes risk, elicitability, and the Expected Shortfall. Mathematical Finance, 31(4), 1190-1217.

Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. Automated machine learning: Methods, systems, challenges, 3-33.

Frees, E. W., Derrig, R. A., & Meyers, G. (Eds.). (2014). Predictive modeling applications in actuarial science (Vol. 1). Cambridge University Press.

Fröhlich, A., & Weng, A. (2018). Parameter uncertainty and reserve risk under Solvency II. Insurance: Mathematics and Economics, 81, 130-141.

Gabrielli, A., Richman, R., & Wüthrich, M. V. (2020). Neural network embedding of the over-dispersed Poisson reserving model. Scandinavian Actuarial Journal, 2020(1), 1-29.

Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659-3667.

Hajilo, R. & Kazemi Boukani, S.E. (2021). The role of artificial intelligence and expert systems in the development of the insurance industry with a literature review approach. The 10th International Conference on Modern Research in Accounting, Management and Humanities in the Third Millennium. Tehran. (in Persian)

Halliwell, L. (2020). Chain Ladder Bias: Its Reason and Meaning. Casualty Acturial Society, 1(2), 214-248.

Hazan, E. & Yang Yuan, K. (2018). Hyperparameter Optimization. ICLR, 1-18.

Hochreiter, J. (1997). Long short-term memory. Neural Computation, MIT Ress.

Kartikasari, M. D., & Imani, N. (2018). Time Series Analysis of Claims Reserve in General Insurance Industry. The 8th Annual Basic Science International Conference (pp. 227-233). Maglang, Indonesia: ISSN.

Khalid, R., & Javaid, N. (2020). A survey on hyperparameters optimization algorithms of forecasting models in smart grid. Sustainable Cities and Society, 61, 102275.

Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia Cirp, 99, 650-655.

Merity, S., Keskar, N. S., & Socher, R. (2017). Regularizing and optimizing LSTM language models. arXiv preprint arXiv:1708.02182.

Peixeiro, M. (2022). Time Series Forecasting in Python. NY: Manning.

Peterson, T. (1984). Loss Reserving. Property/Casualty Insurance. The Journal of Risk and Insurance, 114-121.

Poufinas, T., Gogas, P., Papadimitriou, T., & Zaganidis, E. (2023). Machine learning in forecasting motor insurance claims. Risks, 11(9), 164.

Sharifi, M., Sarbakhshian, A. & Rashidi, A. (2018). Application of machine learning algorithms in life insurance risk prediction. 25th National Conference on Insurance and Development. Tehran, Iran. (in Persian)

Sohrabi, B., Raeesi Vanani, I. & Ghanooni Shishone, V. (2015). Evaluating the Corporate Tax Performance and Analyzing the Tax Trends through the Utilization of Data Mining Algorithms. Financial Research Journal, 17(2), 219-238. doi: 10.22059/jfr.2015.57311 (in Persian)

Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91.

Taylor, G. & McGuire, G. (2016). Stochastic loss reserving using generalized linear models. CAS Monograph, 3, 1-112.

Weindorfer, B. (2012). A Practical guide to the use of the Chain Ladder Method for Determining technical provisions for outstanding claims in non life insurance. University of Applied Sciences of Vienna.

Wüthrich, M. (2018). Neural networks applied to chain-ladder reserving. European Actuar Journal, 8, 407–436.

Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100.

Yilmaz, D., & Büyüktahtakın, İ. E. (2023, June). Learning optimal solutions via an LSTM-optimization framework. In Operations Research Forum (Vol. 4, No. 2, p. 48). Cham: Springer International Publishing.

Zhang, L., Ji, T., Yu, S., & Liu, G. (2023). Accurate prediction approach of SOH for lithium-ion batteries based on LSTM method. Batteries, 9(3), 177.

Ziyadi, H. , Salavati, E. , & Lotfi Heravi, M. M. (2023). Housing Price Forecasting Using AI (LSTM). Financial Research Journal, 25(4), 557-576. doi: 10.22059/frj.2023.349924.1007398 (in Persian)