A Hybrid Swarm-Based System for Commodity Price Forecasting During The Covid-19 Pandemic
نویسندگان
چکیده
Commodity price forecasting is an essential task for defining economic strategies to reduce the impact of inflation in several countries. During covid-19 pandemic, commodity prices have increased due external factors such as influence brokers, traders, policymakers, and investors. Accurate forecasts are increase quality decision-making processes governments order alleviate negative impacts inflation. Forecasting challenging dynamic characteristics influences. In this work, we presented a hybrid swarm-based system combining Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model Support Vector Regression (SVR) optimized by Particle Swarm Optimization (PSO). proposed method, PSO algorithm selects most relevant ARIMAX optimizes SVR parameters. The experiments were conducted on various time series variables, single models methods literature. method significantly reduced RMSE MAPE metrics compared other approaches. selected features new cases, cumulative deaths, dollar exchange rate.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3293738