Hybrid ARFIMA-LRNN Model for Forecasting Commodity Prices - Debopam Rakshit; Ranjit Kumar Paul (Journal of the Indian Society of Agricultural Statistics, 2025)
The unexpected fluctuation of prices of agricultural commodities may have impactful repercussions on the producers. The price volatility can be modeled by applying time series analysis. A time series consists of linear and non-linear components. The linear component can be modeled by the autoregressive integrated moving average (ARIMA) methodology. Again, long-term dependencies amongst the realizations of any time series can also be observed. This long-term dependency can be addressed by incorporating fractional differencing in the ARIMA model which is known as the autoregressive fractionally integrated moving average (ARFIMA) model. To address both the linear and non-linear components effectively, hybrid time series models can be used. In a hybrid model, more than one model is clubbed together such that one is used for capturing the linear component and another captures the nonlinear counterpart. In this article, a hybrid ARFIMA-LRNN (Layer recurrent neural network) model is employed for modeling the price series of arhar for the Mumbai market. The forecasting accuracy of the hybrid model outperformed the standalone models.