Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine

Crude oil price has a large influence on global environment and economy of the country. Similarly, gold price has also impact on economy and it reflects the economy strength of a country. So, the prediction of nonlinear oil and gold prices is the most prominent and arduous factor. In this work, Cluster-based Quasi oppositional based Crow Search Algorithm (CQCSA) optimized Extreme Learning Machine-Fuzzy Inference System (ELMFIS) is proposed for oil and gold price prediction. The consolidation of FIS with ELM is established with benefits of better capability to handle nonlinear data and improved adaptation. The relevant parameters of ELMFIS are elected by CQCSA technique. The CSA algorithm is modified to CQCSA by converting the drawbacks of CSA into the capability to solve a complex problem with high dimension efficiently. The proposed learning algorithm performance is compared with CSA ELMFIS, ELMFIS, ANFIS and ELM models by conceding the performance measures and hypotheses tests. The result suggests that the proposed predictive model can be a promising technique to predict and analysis of oil and gold price.