KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/84497
Title: | Time Series Modelling and Forecasting in agriculture: Basic to Advanced |
Other Titles: | Not Available |
Authors: | Amit Saha K. N. Singh Ramasubramanian, V. Mrinmoy Ray Kanchan Sinha Santosha Rathod D. K. Yadav S. G. Patil Monica Devi |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | Central Sericultural Research and Training Institute, Mysore, Karnataka, India ICAR-Indian Agricultural Statistics Research Institute, New Delhi ICAR-Indian Institute of Rice Research, Hyderabad, Telangana, India The National Institute of health & family Welfare, New Delhi, India Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India Chaudhary Charan Singh Haryana Agricultural University, Hisar |
Published/ Complete Date: | 2020-07-01 |
Project Code: | Not Available |
Keywords: | Forecasting, time series, ARIMA, Agricultural Production, Planning and Policy |
Publisher: | New Delhi Publishers |
Citation: | Not Available |
Series/Report no.: | Not Available; |
Abstract/Description: | Forecasting in agriculture is a formidable challenge, more so when it needs to be based on objective, systemic methods also having the aspects like timeliness and accuracy. Its need and usefulness for farmers, planners, researchers, agribusiness firms and other stakeholders need hardly be emphasized. Because of the significance of the agricultural production in a nation's security, every government utilizes sound methodologies at the institutes established by them and they are not only producers but are also main users of agricultural forecasts. The present chapter describes about the various forecasting techniques using time series data in agricultural domain. Along with basics of time series models, a brief overview of some advanced forecasting methodologies are also described. Some possible areas of agriculture are also highlighted in which such forecasting methodologies are useful for effective for policy making. To start with an introduction to the preliminaries of time series and the premise upon which such data rest are discussed. Then the importance of time series in the context of agriculture is outlined by also listing the various related modeling approaches available in the literature. The art of time series model building by considering the very famous and widely used Auto-Regressive Integrated Moving Average (ARIMA) is then elaborated through its chief stage of identification, estimation and diagnostic checking and forecast performance measures. This is followed by explaining about how indicator variables are used for incorporating the various types like step, pulse and ramp interventions leading to ARIMA. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Book chapter |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Advanced Agriculture by Sagar Maitra and Biswajit Pramanick |
Volume No.: | Not Available |
Page Number: | Not Available |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | Not Available |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/84497 |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1. Book Chapter 1.pdf | 3.81 MB | Adobe PDF | View/Open |
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.