Wavelet analysis for Forecasting Prices and Arrivals of Black Pepper in Karnataka, India

Abstract

Accurate forecasting of Prices and Arrivals of Black pepper is vital for planning and policy purposes. An attempt is made here for modeling and forecasting of Prices and Arrivals of Black pepper for Bengaluru and Somwarpet markets time-series data by using the promising nonparametric methodology of Wavelet analysis in frequency domain. Maximal overlap discrete wavelet transform (MODWT) which, unlike discrete wavelet transform (DWT), does not require the number of data points to be a power of two is employed. Haar wavelet filter is used for computing the same in order to analyze the behaviour of time-series data in terms of different times and scales. Wavelet methodology in frequency domain and Autoregressive integrated moving average (ARIMA) methodologies are applied for different wavelet decomposition levels, at each stage of decomposing we found the best ARIMA models and we compute five month ahead forecasts for hold-out data. Relevant computer programs are developed in SAS, Ver. 9.3 and R, Ver. 2.15.0 software packages. Compare the Forecasting performance of both the Markets using Root mean square prediction error (RMSPE), Mean absolute prediction error (MAPE) and mean absolute error (MAE). Wavelet analysis found to be better model in forecasting prices and Arrivals of Black pepper in Bengaluru Market, the values of RMSE, MAE and MAPE obtained were smaller than those in Somwarpet Market.