Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

Gold Price Prediction has always been a fascinating area of study for researchers and decision makers who wish to determine its future value efficiently and accurately. In this study a predictor model is designed using a Pi-Sigma Neural Network (PSNN) for prediction of future gold price. Two evolutionary estimation paradigms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are suggested during training step of the Pi-Sigma Neural Network to optimize the tunable weights of the network. The model is evaluated based on certain performance evaluation criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) over two dataset such as Gold/INR and Gold/AED accumulated within the same period of time. The result analysis illustrates the better prediction