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Traditional time-series forecasting methods often struggle to simultaneously capture cross-scale nonlinear fluctuations and long-range temporal dependencies, which leads to limited accuracy in short-term electricity price prediction for spot markets, especially when prices exhibit spikes, volatility clustering, and pronounced non-stationarity.
To address these challenges, this study proposes a short-term electricity price forecasting framework based on a fused global-residual Mamba model that combines series decomposition, dual-branch selective state-space encoding, and global residual learning to strengthen representation power and improve training stability. First, a moving average filter is applied to the normalized electricity price sequence to decouple it into a trend component and a residual component, separating relatively stable low-frequency movements from high-frequency stochastic variations. The decomposed sequences are concatenated along the temporal dimension and mapped through a high-dimensional embedding layer to obtain a richer latent representation capable of characterizing complex market dynamics. To better reflect the multi-factor formation mechanism of spot prices, the model also incorporates exogenous variables, such as regional load and weather-related information (e.g., temperature and meteorological conditions). Building on these inputs, a parallel dual-branch Mamba encoder is designed to extract local-to-global dynamic features from complementary perspectives. The variable-correlation branch focuses on learning time-varying interdependencies between electricity prices and exogenous drivers, explicitly modeling cross-variable coupling and market co-movements. In parallel, the feature-interaction branch targets nonlinear transformations and interactions within the embedded feature space; by permuting tensor dimensions so that selective scanning operates along the embedding dimension rather than only along time, it uncovers abstract interaction patterns that conventional temporal scanning may overlook. To integrate heterogeneous information from both branches, their outputs are concatenated and passed to a global residual learning module, which performs additive fusion between the fused representations and the original embedded input. This global residual pathway provides a stable channel for information flow, alleviates gradient degradation in deeper state-space architectures, and enhances the model’s ability to capture multi-scale patterns by preserving original signals while enriching them with learned cross-variable and cross-feature dynamics. For robust performance and reduced manual effort, Bayesian optimization is applied under a time-series cross-validation (TS-CV) setting to tune key hyperparameters, while training further adopts learning-rate scheduling and early stopping to improve efficiency and stability.
Experiments on real operational data from the Australian Energy Market Operator (AEMO) for 24-hour-ahead forecasting demonstrate clear performance gains: the proposed Residual Mamba reduces RMSE by 29.22% relative to the baseline Mamba model and by 35.33% relative to LSTM, confirming superior accuracy and robustness.
Ablation results further highlight the essential role of the series decomposition module, the importance of the variable-correlation branch in the dual-branch design, and the effectiveness of global residual connections in stabilizing training and improving feature expression for highly volatile spot-market price series.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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