Indian Journal of Agricultural Research

  • Chief EditorV. Geethalakshmi

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

  • NAAS Rating 5.60

  • SJR 0.293

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Indian Journal of Agricultural Research, volume 54 issue 1 (february 2020) : 65-70

Time Series Analysis of Indian Spices Export and Prices

S. Anusha, B. Srinivasa Kumar, D. Satish Kumar
1Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522 502, Andhra Pradesh, India.
Cite article:- Anusha S., Kumar Srinivasa B., Kumar Satish D. (2019). Time Series Analysis of Indian Spices Export and Prices. Indian Journal of Agricultural Research. 54(1): 65-70. doi: 10.18805/IJARe.A-5283.
India is the land of spices and is the largest producer, consumer and the exporter of spices in the world. Spices are an important component of Indian Agricultural Exports earning valuable foreign exchange and are the source of livelihood for millions of small and marginal farmers across different states of the country. Modeling of agricultural exports in general and spices exports in particular is important in the contest of spices exports being a priority area for Indian policy makers. Time series modeling of agricultural commodity exports is an active area of research in recent times. Generally Box Jenkins approach (ARIMA) is the referred technique for this purpose. When data exhibits volatility clustering, ARCH/GARCH models are used .When the data does not support linearity assumptions neutral network models are used. However, real world time series data is believed to be a combination of linear and non-linear patterns. In this context, Hybrid models which are a combination of AR models and Artificial Neural Networks are providing more accurate forecasts.  The present study, using  secondary data for the period  from 1960-61 to 2017-18 applies three hybrid models  for forecasting  Indian spices exports both in terms of volume and prices. Based on the RMSE each model is evaluated and finally model with least RMSE was selected for forecasting both volume and unit prices of total spices export for the coming 10 years (2018-19 to 2027-28). Analysis of data was done with the help of open source software R. Results from the study show that, Hybrid model consisting of ARIMA, Exponential Smoothing and Tbats Model with unequal weights was found to be the best model on the basis of RMSE for forecasting Indian spices exports. Thus, for both forecasting and policy formulation the hybrid model is recommended.
  1. Annesha,M. (2017). An analysis of growth trend, instability and determinants of rice production in Assam. Indian Journal of Agricultural Research. 51: 355-359.
  2. Bhavani Devi,I., Srikala,M. and Ananda,T. (2016). Price behaviour of chillies in Guntur market of Andhra Pradesh, India. Indian Journal of Agricultural Research. 50: 471-474.
  3. Chongli, Di., Xiaohua, Y., Xiaochao, W. (2014). A Four-Stage hybrid model for hydrological time series forecasting. PLOS ONE. 9(8): p.e104663.
  4. Dipankar, M. and Ranjit Kumar, P. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model Assisted Statistics and Applications. 12: 255-264.
  5. HuiZou, Yuhong, Y. (2004). Combining time series models for forecasting. International Journal of Forecasting. 20: 69- 84.
  6. Khashei, M. and Bijari, M. (2011). A new hybrid methodology for nonlinear Time Series Forecasting. Modelling and Simulation in Engineering. 2011: 1-5.
  7. Panigrahi, S. and Behera, H. S. (2017). A hybrid ETS–ANN model for time series forecasting. Engineering Applications of Artificial Intelligence. 66:49-59.
  8. Peter Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50:159–175.
  9. Savita, P. and Anil, K.D. (2018). Trend analysis of production and productivity of major crops and its sustainability: A case study of Haryana. Indian Journal of Agricultural Research. 52: 571-575.
  10. Sudhakarrao, M.S. and Katkade, J.L. (2016). Seasonality and volatility in arrivals and prices of oilseeds in Marathwada region of Maharashtra state, India. Indian Journal of Agricultural Researc. 50: 8-14.
  11. Wang, Li., HaofeiZou, Jia Su, Ling Li and Sohail, Ch. (2013). An ARIMA-ANN Hybrid Model for Time Series Forecasting. Systems Research and Behavioral ScienceSyst. 30: 244-259.

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