QURUQ IQLIM SHAROITLARIDA QISHLOQ XOʻJALIGI ISHLAB CHIQARISHINI MASSHTABLASH USULLARI ASOSIDA BAHOLASH

QURUQ IQLIM SHAROITLARIDA QISHLOQ XOʻJALIGI ISHLAB CHIQARISHINI MASSHTABLASH USULLARI ASOSIDA BAHOLASH

Authors

  • Sayyora Qulmatova Toshkent davlat iqtisodiyot universiteti

Keywords:

Holt-Winter, StandardScaler, MinMaxScaler, RobustScaler, Normalizer.

Abstract

Maqolada Holt-Winter usuli yordamida eksponensial tekislash va optimallashtirish asosida qishloq xo'jaligi mahsulotlarining ishlab chiqarishini baholash amalga oshirilgan. Ma'lumotlarni masshtablash ma'lumotlar to'plamidagi optimallashtirish algoritmlarini oldindan qayta ishlash bosqichlaridan biridir. Optimallashtirish usullarining samaradorligi o'rtacha foiz xatosi (MAPE) bilan o'lchanadi. 

References

Achmad Muchayan. (2019). Comparison of Holt and Brown's Double Exponential Smoothing Methods in The Forecast of Moving Price for Mutual Funds Journal of Applied Science, Engineering, Technology, and Education Vol. 1 No. 2 (2019) https://doi.org/10.35877/454RI.asci1167.

Utami, R., & Atmojo, S. (2017). Perbandingan Metode Holt Exponential Smoothing dan Winter Exponential Smoothing Untuk Peramalan Penjualan Souvenir. 11(2), 123–130.

Güzin Tirkeş, Cenk Güray, Neş’e Çelebi. Demand forecasting: a comparison. Between the Holt-winters, trend analysis and decomposition models ISSN 1330-3651 (Print), ISSN 1848-6339 (Online).

Potocnik P, Strmcnik E, Govekar E. Linear and neural network-based models for short-term heat load forecasting. Stroj vestnik J Mech Eng 2015;61(9): 543e50.

Fildes R, Liao KP. The accuracy of a procedural approach to specifying feedforward neural networks for forecasting. Comput Oper Res 2005; 32:2151-69.

Balkin SD, Ord JK. Automatic neural network modelling for univariate time series. Int J Forecast 2000; 16:509-15.

Gary M.Roodman Exponentially smoothed regression analysis for demand forecasting. Journal of Operations Management Volume 6, Issues 3–4, May–August 1986, Pages 485-497.

Xiaohui He, Ying Nie, Hengliang Guo, and Jianzhou Wang. Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting. DOI 10.1109/ACCESS.2020.2980562, IEEE Acces.

.Sungil Kim, Heeyoung Kim. A new metric of absolute percentage error for intermittent demand forecasts. Volume 32, Issue 3, July–September 2016, Pages 669-679.

Timothy O. Hodson, Thomas M. Over, Sydney S. Foks. Mean Squared Error, Deconstructed. Journal of Advances in Modeling Earth Systems. Nov 23 2021

Ahsan, M.M.; Mahmud, M.A.P.; Saha, P.K.; Gupta, K.D.; Siddique, Z. Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance. Technologies 2021, 9, 52. https://doi.org/10.3390/technologies9030052.

Pawlovsky, A.P. An ensemble based on distances for a kNN method for heart disease diagnosis. In Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA, 24–27 January 2018; pp. 1–4.

Bashir, S.; Khan, Z.S.; Khan, F.H.; Anjum, A.; Bashir, K. Improving Heart Disease Prediction Using Feature Selection Approaches. In Proceedings of the 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 8–12 January 2019; pp. 619–623.

Bhatia, S.; Prakash, P.; Pillai, G. SVM-based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA, 22–24 October 2008; pp. 34–38.

Guide, M.; Wankhade, K.; Dongre, S. Decision support system for heart disease based on support vector machine and artificial neural network. In Proceedings of the 2010 International Conference on Computer and Communication Technology (ICCCT), Allahabad, India, 17–19 September 2010; pp. 741–745.

Number, S.; Patil, C.; Ghatol, A. Heart disease diagnosis using support vector machine. In Proceedings of the International Conference on Computer Science and Information Technology (ICCSIT'), Pattaya, Thailand, 17–18 December 2011.

Take H. Improvement of heart attack prediction by the feature selection methods. Turk. J. Electr. Eng. Comput. Sci. 2018, 26, 1–10. [CrossRef].

Amin, M.S.; Chiam, Y.K.; Varathan, K.D. Identification of significant features and data mining techniques in predicting heart disease. Telemat. Inform. 2019, 36, 82–93. [CrossRef].

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Published

2024-10-17

How to Cite

QURUQ IQLIM SHAROITLARIDA QISHLOQ XOʻJALIGI ISHLAB CHIQARISHINI MASSHTABLASH USULLARI ASOSIDA BAHOLASH. (2024). Scientific Journal of Actuarial Finance and Accounting, 4(09), 144-155. https://doi.org/10.55439/AFA/vol4_iss09/556