CROP PRODUCTION UNDER DIFFERENT CLIMATIC CONDITIONS BY ANALYZING AGRICULTURAL DATA USING MULTIPLE LINEAR REGRESSION, WINTER HOLT, AND ARTIFICIAL INTELLIGENCE

Author:

Qulmatova Sayyora1ORCID,Karimov Botirjon1ORCID,Abdullayev Munis1ORCID,Karimova Shirin1ORCID

Affiliation:

1. Department of Digital Economy, Tashkent State University of Economics, Uzbekistan

Publisher

ACM

Reference33 articles.

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

3. 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). 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).

4. 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. 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.

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

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