Enhancing Forecasting Accuracy of Palm Oil Import to India Using Machine Learning Techniques (Record no. 133734)

MARC details
000 -LEADER
fixed length control field 02251nam a2200253Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240826s9999||||xx |||||||||||||| ||und||
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0019-5014, 2582-7510
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Reddy, A. Amarender
9 (RLIN) 120571
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Sarada, C.
9 (RLIN) 120572
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Sreenivasulu, K. N.
9 (RLIN) 120573
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Srinivasa Rao, V.
9 (RLIN) 120574
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name 2024
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Naga Latha, K.
9 (RLIN) 120575
245 #0 - TITLE STATEMENT
Title Enhancing Forecasting Accuracy of Palm Oil Import to India Using Machine Learning Techniques
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. The Indian Journal of Agricultural Economics
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent 214-230
520 ## - SUMMARY, ETC.
Abstract Forecasting of palm oil imports to India has gained significant prominence in contemporary times due to huge exchequer is spending on vegetable oil imports. The government is keen on reducing imports. The quantity to be imported in future years is utmost important to make any policies or programs to enhance the oilseeds production. For forecasting ARIMA models have been the most widely used technique during the last few decades. When the assumption of homoscedastic error variance is violated then ARCH/GARCH models are applied to capture the changes in the conditional variance of the time-series data. The machine learning techniques, i.e., ANN and SVR, can also be applied in the field of forecasting of real time-series data successfully as an alternative to the traditional forecasting models as these are data-driven models and could capture nonlinearities existing in the data. The present study analyzed monthly time series data of palm oil import volume (thousand tonnes) from the world to India from April 2007 to March 2023. It is clear from the results that the machine learning models, viz, SVR and ANN, outperformed the traditional time series models (GARCH and ARIMA) with the least RMSE, MAPE, Theil's U statistic and the highest CDC values for both training and testing datasets. Empirical results revealed that SVR is the best model for forecasting palm oil import volume compared to all other models.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ANN
9 (RLIN) 120576
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Palm Oil Import
9 (RLIN) 120577
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element SVR
9 (RLIN) 120578
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
9 (RLIN) 71338
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://isaeindia.org/wp-content/uploads/2024/07/03-C.Sarada.pdf">https://isaeindia.org/wp-content/uploads/2024/07/03-C.Sarada.pdf</a>
999 ## - SYSTEM CONTROL NUMBERS (KOHA)
Koha biblionumber 133734
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        Dr VKRV Rao Library Dr VKRV Rao Library 27/08/2024 Vol. 79, No. 2   AI385 27/08/2024 27/08/2024 Article Index