Modelling and forecasting cotton production using tuned-support vector regression

被引：2

作者：

Saha, Amit ^{[1
]}

Singh, K. N. ^{[2
]}

Ray, Mrinmoy ^{[2
]}

Rathod, Santosha ^{[3
]}

Choudhury, Sharani ^{[4
]}

机构：

[1] Cent Silk Board, Cent Sericultural Res & Training Inst, Srirampura 570008, Mysuru, India

[2] ICAR Indian Agr Stat Res Inst, New Delhi 110012, India

[3] ICAR Indian Inst Rice Res, Hyderabad 500030, India

[4] ICAR Indian Agr Res Inst, New Delhi 110012, India

来源：

CURRENT SCIENCE | 2021年 / 121卷 / 08期

关键词：

ARIMA; cotton production forecasting; SVR; time series; tuned-SVR; PREDICTION;

D O I：

10.18520/cs/v121/i8/1090-1098

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton production compared to auto regressive integrated moving average and classical SVR models.

引用

页码：1090 / 1098

页数：9

共 50 条

[21] Support vector regression modelling and optimization of energy consumption in carbon fiber production line
Golkarnarenji, Gelayol
Naebe, Minoo
Badii, Khashayar
Milani, Abbas S.
Jazar, Reza N.
Khayyam, Hamid
COMPUTERS & CHEMICAL ENGINEERING, 2018, 109 : 276 - 288
[22] Chaos-based support vector regression for load power forecasting of excavators
Huo, Dongyang
Chen, Jinshi
Wang, Tongyang
EXPERT SYSTEMS WITH APPLICATIONS, 2024, 246
[23] Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm
Chen, Rong
Liang, Chang-Yong
Hong, Wei-Chiang
Gu, Dong-Xiao
APPLIED SOFT COMPUTING, 2015, 26 : 435 - 443
[24] Temperature dependent modelling of magnetorheological (MR) dampers using support vector regression
Priya, C. Bharathi
Gopalakrishnan, N.
SMART MATERIALS AND STRUCTURES, 2019, 28 (02)
[25] Modelling functional additive quantile regression using support vector machines approach
Crambes, Christophe
Gannoun, Ali
Henchiri, Yousri
JOURNAL OF NONPARAMETRIC STATISTICS, 2014, 26 (04) : 639 - 668
[26] Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment
Wu, Jian
Liu, Haixing
Wei, Guozhen
Song, Tianyu
Zhang, Chi
Zhou, Huicheng
WATER, 2019, 11 (07)
[27] Forecasting Cryptocurrency Prices Using Support Vector Regression Enhanced by Particle Swarm Optimization
Parvini, Navid
Ahmadian, Davood
Ballestra, Luca Vincenzo
COMPUTATIONAL ECONOMICS, 2024,
[28] Time series forecasting method of building energy consumption using support vector regression
Liu, Dandan
Chen, Qijun
Mori, Kazuyuki
2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, 2015, : 1628 - 1632
[29] Short-term metro passenger flow forecasting using ensemble-chaos support vector regression
Shi, Zhuangbin
Zhang, Ning
Schonfeld, Paul M.
Zhang, Jian
TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2020, 16 (02) : 194 - 212
[30] Support vector regression with genetic algorithms in forecasting tourism demand
Chen, Kuan-Yu
Wang, Cheng-Hua
TOURISM MANAGEMENT, 2007, 28 (01) : 215 - 226

← 1 2 3 4 5 →