Optimisation-based weighted ensemble algorithm for predicting prices of spices

India plays a significant role in global agriculture production, including spices.Spices play an important role in cultural significance and economic trade relations while providing nutritional and medicinal benefits.The volatility and complexity of the price of spices require improved forecasting methods to support informed decision-making in agricultural markets.Recently, researchers have focused on using the traditional time series model as well as machine learning (ML) model to forecast the price of agricultural commodities.Using a standalone model struggles to capture the complex pattern in time series data.To overcome this challenge, ensemble machine learning approaches based on fixed weight (FW-ensemble) have been proposed.The ML models like artificial neural networks (ANN), random forest (RF), k-nearest neighbours (kNN), extreme gradient boosting (XGBoost), support vector regression (SVR) and the stochastic model, e.g.autoregressive integrated moving average (ARIMA) model have been used.The outputs of these models are ensembled using optimised fixed weights.In this study, the prices of two important spices, namely turmeric and coriander, from 2010 to 2024, collected from AGMARKNET (https:// agmarknet.gov.in/) were considered.The MCS algorithm was used to select the better-performing model.The empirical performance of the ensemble method was compared with that of the stochastic model (ARIMA), ML techniques (ANN, RF, kNN, XGBoost, SVR) and deep learning techniques, e.g.long shortterm memory (LSTM) and gated recurrent unit (GRU), based on several accuracy measures.It revealed that the FW-ensemble approach significantly outperformed the other candidate models in terms of prediction accuracy.