2024, Vol. 9, Issue 2, Part A
An Application of multivariate time series models for forecasting the prices of tomato in Haryana
Author(s): Pushpa Ghiyal and Joginder Kumar
Abstract: Time series forecasting of agricultural products has the basic importance in maintaining the sustainability of agricultural production. In this study, multivariate time series models: vector autoregressive (VAR) and vector autoregressive integrated moving average (VARMA) have been used for modelling the interdependence among the price series of tomato in selected APMC markets of Haryana. The forecasting performance of VAR and VARMA models have also been compared using percentage relative deviation (RD (%)), standard error of prediction (SEP) and mean absolute percent error (MAPE) for different forecast horizons 1, 3, 6, 9 and 12 months. The results suggest that forecast performance of VARMA models is more accurate than VAR models for forecasting the price series of tomato. The findings of the study would help in decision making for managing agricultural supplies and helping to improve the purchasing behavior of consumers.
Pages: 37-42 | Views: 55 | Downloads: 11Download Full Article: Click Here
How to cite this article:
Pushpa Ghiyal, Joginder Kumar. An Application of multivariate time series models for forecasting the prices of tomato in Haryana. Int J Stat Appl Math 2024;9(2):37-42.
