Forecasting time series with extreme events is critical yet challenging due to their high variance, irregular dynamics, and sparse but high-impact nature. While existing methods excel in modeling dominant regular patterns, their performance degrades significantly during extreme events, constituting the primary source of forecasting errors in real-world applications. Although some approaches incorporate auxiliary signals to improve performance, they still fail to capture extreme events' complex temporal dynamics. To address these limitations, we propose M²FMoE, an extreme-adaptive forecasting model that learns both regular and extreme patterns through multi-resolution and multi-view frequency modeling. It comprises three modules: (1) a multi-view frequency mixture-of-experts module assigns experts to distinct spectral bands in Fourier and Wavelet domains, with cross-view shared band splitter aligning frequency partitions and enabling inter-expert collaboration to capture both dominant and rare fluctuations; (2) a multi-resolution adaptive fusion module that hierarchically aggregates frequency features from coarse to fine resolutions, enhancing sensitivity to both short-term variations and sudden changes; (3) a temporal gating integration module that dynamically balances long-term trends and short-term frequency-aware features, improving adaptability to both regular and extreme temporal patterns. Experiments on real-world hydrological datasets with extreme patterns demonstrate that M²FMoE outperforms state-of-the-art baselines without requiring extreme-event labels.

Accurate power load forecasting in industrial parks is crucial for optimizing energy management and operational efficiency. Existing models struggle with industrial load series' complex, multi-target nature and the need to integrate diverse exogenous variables. This paper introduces the Temporal Structure-Preserving Transformer (TSPT), a novel architecture that addresses these challenges by decomposing multi-target series into univariate series, enabling parallel processing and integrating exogenous data. The TSPT model incorporates the Gated Feature Fusion (GFF), which learns to capture multiscale temporal patterns from each target sequence and exogenous factors by preserving the temporal structure of the series. This parallel processing and the structure-preserving transformations allow TSPT to effectively integrate domain-specific knowledge, such as weather, production, and efficiency data, enhancing its forecasting performance. Comprehensive experiments on a real-world industrial park dataset demonstrate TSPT's superiority over state-of-the-art methods in handling complex, multi-target forecasting tasks with integrated exogenous variables. The proposed approach offers a pathway for scalable and accurate load forecasting in industrial settings, improving energy management and operational decision-making.

Forecasting carbon futures prices is a challenging task due to the complex and dynamic factors influencing them. Accurate forecasting can aid carbon market participants in hedging and optimizing their trading strategies. In this paper, we propose a novel feature selection method based on importance measures, aimed at selecting the most relevant and informative features for forecasting carbon futures prices. Our method introduces Gaussian noise to the input features, calculates the importance scores of the features, and determines the optimal threshold value for feature selection. We train and test different forecasting models on both the original and noisy feature sets using a 5-fold cross-validation approach. The importance score of each feature is calculated based on the error difference between the original and noisy feature sets. The optimal threshold value is determined based on the minimum prediction error obtained by ranking the features. We combine our feature selection method with different models to forecast carbon futures prices. The experimental results demonstrate that our method can effectively select useful features, outperforming variance thresholding and analysis of variance in feature selection. Moreover, our feature selection approach improves the prediction accuracy of different models. Our method is also robust in enhancing prediction accuracy across different models, test sets, time periods, and Gaussian noise levels.

This paper introduces a novel feature selection method, called Feature Selection based on Importance Measures (FS-IM), to enhance the forecasting of crude oil returns. FS-IM innovatively combines active learning with the application of Gaussian noise to input features and selects the most relevant features using an optimal threshold value. The paper applies a ridge regression (RR) model based on FS-IM (FS-RR) to identify the factors that have important information for crude oil return forecasting. The paper compares FS-IM with other dimension reduction methods such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Independent Component Analysis (ICA). The results show that FS-IM can significantly improve model accuracy, demonstrating its effectiveness in finding key features. Moreover, FS-IM is more stable and consistent than other dimension reduction methods in enhancing the prediction accuracy in different scenarios, indicating its superior capability in capturing complex relationships between input and output variables. Furthermore, this study compares FS-RR model with other 13 prediction models by conducting experiments using a series of evaluation metrics, different statistical tests, and different step-ahead predictions and training sets. The results confirm that the RR model based on FS-IM can consistently outperform other model in terms of predictive performance and economic value, proving its effectiveness and robustness. This study contributes to the literature on crude oil price forecasting by addressing the challenges of high-dimensional and complex data, and by providing a robust, practical tool for professionals in energy economics and finance.

This paper presents an offset-free model predictive controller for fast and accurate control of a spherical soft robotic arm. In this control scheme, a linear model is combined with an online disturbance estimation technique to systematically compensate model deviations. Dynamic effects such as material relaxation resulting from the use of soft materials can be addressed to achieve offset-free tracking. The tracking error can be reduced by 35% when compared to a standard model predictive controller without a disturbance compensation scheme. The improved tracking performance enables the realization of a ball catching application, where the spherical soft robotic arm can catch a ball thrown by a human.

Abstract The prediction model is a major component within public health cyber‐physical systems, which supports decisions on prevention and control of diseases. Hand, foot, and mouth disease (HFMD) is one of the most common global infectious diseases with the highest incidence rate. Previous HFMD prediction models are mainly based on the time series that counted in equal‐grained time intervals. However, there are details in the time series counted in fine‐grained time intervals. To benefit from both equal‐grained and fine‐grained data, we proposed a dual‐grained representation (DGR) model. The DGR first represents inputted data to temporal patterns. Then, the represented patterns are consolidated to generate predictions. Experimental comparisons of the short‐term prediction performance are figured out by using real outpatient collections in Xiamen, China.

Underground hydrocarbon reservoir rocks comprise numerous multiscale irregular pores that significantly affect the mechanical and fluid transport properties of the rock. It is considerably challenging for in situ geological monitoring and laboratory tests to accurately characterize the changes in the interior structure and the corresponding mechanical properties of the rock mass during dynamic excavation processes. The 3-D numerical reconstruction models that are based on the statistical information extracted from X-ray computed tomography (XCT) images provide a feasible method to obtain and characterize the interior pore structures and their effects on the physical responses of reservoir rocks. However, obtaining sufficient high-resolution 2-D XCT images is economically expensive by the traditional fan beam CT scan system. Reconstructing 3-D porous structures by computational methods using statistical information extracted from XCT images usually has low efficiency. Therefore, in this paper, we introduce a novel method to numerically reconstruct natural sandstone rock using a small number of 2-D XCT images. The Bayesian information criterion was used to determine the minimum number of 2-D XCT images required to ensure the expected reconstruction accuracy. A multithread parallel reconstruction scheme was employed to improve the efficiency. The accuracy of the proposed method was verified by comparing the statistical correlation functions, geometrical and topological characteristics, and mechanical properties of pore structures between the reconstructed model and a sandstone prototype. This paper provides a method to achieve fast, economic, and accurate 3-D reconstruction of porous rock.