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A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks

Research output: Contribution to specialist publication › Article

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A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. / Hassan, Najam Ul; Khan, Farrukh Zeeshan; Bibi, Hafsa et al.
In: IEEE Consumer Electronics Magazine, Vol. 10, No. 6, 01.11.2021, p. 45-52.

Research output: Contribution to specialist publication › Article

Harvard

Hassan, NU, Khan, FZ, Bibi, H, Khan, NT, Nayyar, A & Bilal, M 2021, 'A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks' IEEE Consumer Electronics Magazine, vol. 10, no. 6, pp. 45-52. https://doi.org/10.1109/MCE.2021.3063547

APA

Hassan, N. U., Khan, F. Z., Bibi, H., Khan, N. T., Nayyar, A., & Bilal, M. (2021). A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. IEEE Consumer Electronics Magazine, 10(6), 45-52. https://doi.org/10.1109/MCE.2021.3063547

Vancouver

Hassan NU, Khan FZ, Bibi H, Khan NT, Nayyar A, Bilal M. A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. IEEE Consumer Electronics Magazine. 2021 Nov 1;10(6):45-52. doi: 10.1109/MCE.2021.3063547

Author

Hassan, Najam Ul ; Khan, Farrukh Zeeshan ; Bibi, Hafsa et al. / A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks. In: IEEE Consumer Electronics Magazine. 2021 ; Vol. 10, No. 6. pp. 45-52.

Bibtex

@misc{f3c78e015a7a4dcbb5d9822ab5ea6838,
title = "A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks",
abstract = "Agricultural industry contributes to the economic backbone of many countries. Major crops like wheat, cotton, and rice stand out as fulfillment for basic commodities as well as profitable crops. Naturally, the consumption of major crops is increasing every year, influencing many countries to import the staple crops to meet the nutritional requirements of individuals, and thereby, keeping pressure on the economies for the years ahead. This research work addresses the development of an accurate consumption forecasting model for time series data. The proposed methodology uses 18 socio-economic and environmental factors and evaluates their impact on major crop consumption in Pakistan. Most influential factors are differentiated by the Linear Regression Model to forecast next year's upshot. The smart results of the model are beneficial for the farmers to cope with the decisive question of next pragmatic crop. The proposed model was compared with a variant of conventional approaches and verified the efficient performance in terms of forecast accuracy.",
author = "Hassan, {Najam Ul} and Khan, {Farrukh Zeeshan} and Hafsa Bibi and Khan, {Nokhaiz Tariq} and Anand Nayyar and Muhammad Bilal",
year = "2021",
month = nov,
day = "1",
doi = "10.1109/MCE.2021.3063547",
language = "English",
volume = "10",
pages = "45--52",
journal = "IEEE Consumer Electronics Magazine",
issn = "2162-2248",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - GEN

T1 - A Decision Support Benchmark for Forecasting the Consumption of Agriculture Stocks

AU - Hassan, Najam Ul

AU - Khan, Farrukh Zeeshan

AU - Bibi, Hafsa

AU - Khan, Nokhaiz Tariq

AU - Nayyar, Anand

AU - Bilal, Muhammad

PY - 2021/11/1

Y1 - 2021/11/1

N2 - Agricultural industry contributes to the economic backbone of many countries. Major crops like wheat, cotton, and rice stand out as fulfillment for basic commodities as well as profitable crops. Naturally, the consumption of major crops is increasing every year, influencing many countries to import the staple crops to meet the nutritional requirements of individuals, and thereby, keeping pressure on the economies for the years ahead. This research work addresses the development of an accurate consumption forecasting model for time series data. The proposed methodology uses 18 socio-economic and environmental factors and evaluates their impact on major crop consumption in Pakistan. Most influential factors are differentiated by the Linear Regression Model to forecast next year's upshot. The smart results of the model are beneficial for the farmers to cope with the decisive question of next pragmatic crop. The proposed model was compared with a variant of conventional approaches and verified the efficient performance in terms of forecast accuracy.

AB - Agricultural industry contributes to the economic backbone of many countries. Major crops like wheat, cotton, and rice stand out as fulfillment for basic commodities as well as profitable crops. Naturally, the consumption of major crops is increasing every year, influencing many countries to import the staple crops to meet the nutritional requirements of individuals, and thereby, keeping pressure on the economies for the years ahead. This research work addresses the development of an accurate consumption forecasting model for time series data. The proposed methodology uses 18 socio-economic and environmental factors and evaluates their impact on major crop consumption in Pakistan. Most influential factors are differentiated by the Linear Regression Model to forecast next year's upshot. The smart results of the model are beneficial for the farmers to cope with the decisive question of next pragmatic crop. The proposed model was compared with a variant of conventional approaches and verified the efficient performance in terms of forecast accuracy.

U2 - 10.1109/MCE.2021.3063547

DO - 10.1109/MCE.2021.3063547

M3 - Article

AN - SCOPUS:85102316820

VL - 10

SP - 45

EP - 52

JO - IEEE Consumer Electronics Magazine

JF - IEEE Consumer Electronics Magazine

SN - 2162-2248

ER -