Forecasting of monthly cardamom price using long memory time series modelling technique

Cardamom is an important spice crop known for its aromatic qualities. Kerala is a major producer state of small cardamom in India. This study aimed to develop a long memory time series model for forecasting of monthly average farm wholesale price of small cardamom in Kerala. Monthly price data collected from Directorate of Economics and Statistics, Government of Kerala for the period January 2007 to December 2020. Upon analysis of data, ARFIMA (0, 0.22, 1) model was selected which in turn compared with traditional ARIMA model. The results emphasised the superior performance of ARFIMA (0, 0.22, 1) model over ARIMA model in forecasting the long memory price time series of small cardamom.

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Publication Details

Journal
International Journal of Statistics and Applied Mathematics
Published
2024-11-01
DOI
https://doi.org/10.22271/maths.2024.v9.i6b.1906
Citations
1
Primary Topic
Currency Recognition and Detection
Type
article
Field-Weighted Citation Impact
0.50
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Forecasting of monthly cardamom price using long memory time series modelling technique

Debasis Bhattacharya, Digvijay Singh Dhakre, Kader Ali Sarkar, Muhammed Irshad M
1 citations
International Journal of Statistics and Applied Mathematics
Currency Recognition and Detection
0.50
article

Forecasting of monthly cardamom price using long memory time series modelling technique

Debasis Bhattacharya, Digvijay Singh Dhakre, Kader Ali Sarkar, Muhammed Irshad M
article en
1 citations

Abstract

Cardamom is an important spice crop known for its aromatic qualities. Kerala is a major producer state of small cardamom in India. This study aimed to develop a long memory time series model for forecasting of monthly average farm wholesale price of small cardamom in Kerala. Monthly price data collected from Directorate of Economics and Statistics, Government of Kerala for the period January 2007 to December 2020. Upon analysis of data, ARFIMA (0, 0.22, 1) model was selected which in turn compared with traditional ARIMA model. The results emphasised the superior performance of ARFIMA (0, 0.22, 1) model over ARIMA model in forecasting the long memory price time series of small cardamom.

International Journal of Statistics and Applied MathematicsVol. 9(6)
Openalex Percentile: Top 33%
Currency Recognition and Detection
0.50
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