A Hybrid Model of MEMD and PSO-LSSVR for Steel Price Forecasting
نویسندگان
چکیده
Herein, we propose a novel hybrid method for forecasting steel prices by modeling nonlinearity and time variations together to enhance adaptability. The multivariate empirical mode decomposition (MEMD)–ensemble-EMD (EEMD) approach was employed preprocessing separate the nonlinear variation components of hot-rolled coil (HRC) price return series, particle swarm optimization (PSO)-based least squares support vector regression (LSSVR) generalized autoregressive conditional heteroskedasticity (GARCH) model were applied capture characteristics returns, respectively. results revealed that compared with traditional models, proposed yields superior performance HRC returns. evidence also suggested in capturing dynamics during COVID-19 pandemic period, asymmetric GARCH MEMD–LSSVR outperformed not only standard models but EEMD-LSSVR models. MEMD–LSSVR–GARCH provides useful decision tool steelmakers consumers evaluate trends.
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ژورنال
عنوان ژورنال: International Journal of Engineering and Management Research
سال: 2022
ISSN: ['2250-0758', '2394-6962']
DOI: https://doi.org/10.31033/ijemr.12.1.5