Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

Abstract

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.

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

“…OSELM has been used in a variety of research fields, including power quality event detection (Sahani et al 2020), time series analysis (Das et al 2019), and stream flow forecasting (Lima et al 2017), and has outperformed basic ELM and other ML techniques as well. Several researchers have addressed the second issue by hybridizing the training of ELM using optimization techniques such as particle swarm optimization (PSO) (Pradeepkumar and Ravi 2017), harmony search (HS) (Dash et al 2014), grey wolf optimization (GWO) (Liu et al 2021), teachinglearning-based optimization (TLBO) (Das and Padhy 2018), crow search algorithm (CSA) (Dash et al 2021), differential evolution (DE) (Abdual-Salam et al 2010), and others. These models not only increase accuracy, but also enhance stability of the model.…”