Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

The economic model derived from the supply and demand of crude oil prices is a significant component that measures development sustainability. Therefore, it essential to mitigate price volatility risks by establishing models will effectively predict prices. A promising approach application long short-term memory artificial neural networks for time-series forecasting. However, their ability tackle complex time series limited. decomposition-forecasting taken. Furthermore, machine learning accuracy highly dependent on hyper-parameter settings. in this paper, modified version salp swarm algorithm tasked with determining satisfying parameters improve performance prediction algorithm. proposed validated real-world West Texas Intermediate (WTI) data throughout two types experiments, one original decomposed after applying variation mode decomposition. In both cases, were adjusted conduct one, three, five-steps ahead predictions. According findings comparative analysis contemporary metaheuristics, was concluded hybrid forecasting, outscoring all competitors.