A Novel Hybrid Nonlinear Forecasting Model for Interval-Valued Gas Prices

This paper proposes a novel hybrid nonlinear interval decomposition ensemble (NIDE) framework to improve forecasting accuracy of interval-valued gas prices. The framework first decomposes the price series using bivariate empirical mode decomposition and interval multiscale permutation entropy to capture dynamics driven by long-term trends, events, and short-term fluctuations. Tailored models are then employed for each component, including a threshold autoregressive interval model, interval event study methodology, and interval random forest. Finally, an ensemble prediction integrates the component forecasts. Empirical results show that the NIDE approach significantly outperforms benchmarks in out-of-sample forecasting of interval-valued natural gas prices. For instance, the RMSE improvements range from 10.3% to 38.8% compared to benchmark models. Additionally, the NIDE approach not only enhances accuracy but also provides economic interpretation by identifying drivers like speculative trading and public interest proxied by online trends.