FORECASTING TEA PRODUCTION IN INDIA: A TIME SERIES APPROACH

被引:0
作者
Deka, Sakuntala [1 ]
Hazarika, P. J. [1 ]
Goswanill, K. [1 ]
Patowary, A. N. [2 ]
机构
[1] Dibrugarh Univ, Dept Stat, Dibrugarh 786004, Assam, India
[2] Assam Agr Univ, Coll Fisheries, Raha 782103, India
来源
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES | 2022年 / 18卷 / 01期
关键词
ARIMA; Forecasting; Tea production; Mean absolute percentage error; Time series analysis;
D O I
暂无
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Tea is one of India's most famous hot beverages and India is the second-largest producer of tea, which results in 23% of the world tea production. The tea industry plays a vital role in the Indian economy. Also, it has a substantial impact on the livelihood of many people employed directly and indirectly by the industry. This research study outlines the development of a conventional time series model, namely the Autoregressive Integrated Moving Average (ARIMA) model for the annual tea production of India. The study used R programming for the analysis. The analyzed data are secondary and obtained from the Tea Board of India, Ministry of Commerce, from 1947 to 2016 with 70 years of data. Based on different diagnostic and evaluation criteria, ARIMA (1,1,2) is the best-fitted forecasting model.
引用
收藏
页码:105 / 112
页数:8
相关论文
共 50 条
  • [31] Forecasting industrial production using structural time series models
    Thury, G
    Witt, SF
    [J]. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 1998, 26 (06): : 751 - 767
  • [32] Time series analysis for psychological research: examining and forecasting change
    Jebb, Andrew T.
    Tay, Louis
    Wang, Wei
    Huang, Qiming
    [J]. FRONTIERS IN PSYCHOLOGY, 2015, 6
  • [33] Forecasting performance of time series models on electricity spot markets
    Guertler, Marc
    Paulsen, Thomas
    [J]. INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2018, 12 (04) : 617 - 640
  • [34] Time series modelling and forecasting of mpox incidence and mortality in Nigeria
    Emmanuel Afolabi Bakare
    Oluwaseun Akinlo Mogbojuri
    Dolapo Oluwaseun Oniyelu
    Afeez Abidemi
    Deborah Oluwatobi Daniel
    Idowu Isaac Olasupo
    Samuel Abidemi Osikoya
    Aaron Onyebuchi Nwana
    Ronke Dorcas Olorunfemi
    Samson Oluwafemi Olagbami
    [J]. BMC Infectious Diseases, 25 (1)
  • [35] Forecasting of Renewable Energy Production in United States: An ARIMA Based Time Series Analysis
    Rajni
    Banerjee, Tuhin
    Kumar, Prashant
    [J]. INTERNATIONAL CONFERENCE ON ADVANCES IN CIVIL ENGINEERING, ICACE 2022, 2024, 3010
  • [36] Modelling and Forecasting Sugarcane and Sugar Production in India
    Vishawajith, K. P.
    Sahu, P. K.
    Dhekale, B. S.
    Mishra, P.
    [J]. INDIAN JOURNAL OF ECONOMICS AND DEVELOPMENT, 2016, 12 (01) : 71 - 79
  • [37] SLIDING SIMULATION - A NEW APPROACH TO TIME-SERIES FORECASTING
    MAKRIDAKIS, S
    [J]. MANAGEMENT SCIENCE, 1990, 36 (04) : 505 - 512
  • [38] Neural network approach to forecasting of quasiperiodic financial time series
    Bodyanskiy, Yevgeniy
    Popov, Sergiy
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 175 (03) : 1357 - 1366
  • [39] Visual Time Series Forecasting: An Image-driven Approach
    Sood, Srijan
    Zeng, Zhen
    Cohen, Naftali
    Balch, Tucker
    Veloso, Manuela
    [J]. ICAIF 2021: THE SECOND ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, 2021,
  • [40] HYBRID NEURAL NETWORKS AS A NEW APPROACH IN TIME SERIES FORECASTING
    Falat, Lukas
    [J]. AD ALTA-JOURNAL OF INTERDISCIPLINARY RESEARCH, 2011, 1 (02): : 134 - 137