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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/46458
Title: Forecasting long range dependent time series with exogenous variable using ARFIMAX model.
Other Titles: Not Available
Authors: Krishna Pada Sarkar
K.N. Singh
A.K. Paul
Ramasubramanian V.
Mukesh Kumar
Achal Lama
Bishal Gurung
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR::Indian Agricultural Statistics Research Institute
Published/ Complete Date: 2020-07-01
Project Code: Not Available
Keywords: Long memory
ARFIMA
ARFIMAX
Publisher: ICAR
Citation: Krishna Pada Sarkar, K N Singh, Amrit Kumar Paul, Rama subramanian V, Mukesh Kumar, Achal Lama and Bishal Gurung(2020). Forecasting long range dependent time series with exogenous variable using ARFIMAX model, Indian Journal of Agricultural Sciences, 90 (7), 1302–5.
Series/Report no.: Not Available;
Abstract/Description: Time series analysis and forecasting is one of the challenging issues of statistical modelling. Modelling of price and forecasting is a vital matter of concern for both the farming community and policy makers, especially in agriculture. Many practical agricultural data, principally commodity price data shows the typical feature of long memory process or long range dependency. For capturing the long memory behavior of the data Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is generally used. Sometimes, in time series data besides the original series, data on some auxiliary or exogenous variables may be available or can be made available with a lower cost; like besides the market prices of commodities, market arrivals for that commodity may be available and it affects the market price of commodities. This type of exogenous variable may be incorporated in existing model to improve the model performance and forecasting accuracy, like Autoregressive Fractionally Integrated Moving Average with exogenous variables (ARFIMAX) model. In the present study undertaken at ICAR-IASRI, New Delhi during 2019, daily maximum and modal price of potato of Agra market of UP, India are taken along with daily market arrival. Both the ARFIMA and ARFIMAX model with market arrival as exogenous variable are applied for the data under study. Comparative studies of the fitted models are employed by using the Relative Mean Absolute Percentage Error (RMAPE) and Root Mean Square Error (RMSE) criteria. We could establish superiority of the ARFIMAX model over the ARFIMA model in terms of modeling and forecasting efficiency.
Description: Not Available
ISSN: 0019-5022
Type(s) of content: Research Paper
Sponsors: Not Available
Language: English
Name of Journal: Indian Journal of Agricultural Sciences
Journal Type: Peer reviewed
NAAS Rating: 6.23
6.21
Impact Factor: 0.23
Volume No.: 90(7)
Page Number: 1302-1305
Name of the Division/Regional Station: Not Available
Source, DOI or any other URL: http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/105599/41366
URI: http://krishi.icar.gov.in/jspui/handle/123456789/46458
Appears in Collections:AEdu-IASRI-Publication

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