A novel machine learning-based electricity price forecasting model based on optimal model selection strategy

Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network

Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction

Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products

The natural gas price is an essential financial variable that needs periodic modeling and predictive analysis for many practical implications. Macroeconomic euphoria and external uncertainty make its evolutionary patterns highly complex. We propose a two-stage granular framework to perform predictive analysis of the natural gas futures for the USA (NGF-USA) and the UK natural gas futures for the EU (NGF-UK) for pre-and during COVID-19 phases. The residuals of the previous stage are introduced as a new explanatory feature along with standard technical indicators to perform predictive tasks. The importance of the new feature is explained through the Boruta feature evaluation methodology. Maximal Overlap Discrete Wavelet Transformation (MODWT) is applied to decompose the original time-series observations of the natural gas prices to enable granular level forecasting. Random Forest is invoked on each component to fetch the respective predictions. The aggregated component-wise sums lead to final predictions. A rigorous performance assessment signifies the efficacy of the proposed framework. The results show the effectiveness of the residual as a feature in deriving accurate forecasts. The framework is highly efficient in analyzing patterns in the presence of a limited number of data points during the uncertain COVID-19 phase covering the first and second waves of the pandemic. Our findings reveal that the prediction accuracy is the best for the NGF-UK in the pre-COVID-19 period. Also, the prediction accuracy of the NGF-USA is better in the COVID-19 period than the pre-COVID-19 period. 

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A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases

Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network

—We develop the theory and algorithmic toolbox for networked federated learning in decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-speciﬁc notions of simi- larity between local datasets. Different notions of similarity are induced by spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate networked federated learning using a generalized total variation minimization. This formulation uniﬁes and considerably extends existing federated multi-task learning methods. It is highly ﬂexible and can be combined with a broad range of parametric models including Lasso or deep neural networks. Our main algorithmic contribution is a novel networked federated learning algorithm which is well suited for distributed computing environments such as edge computing over wireless networks. This algorithm is robust against inexact computations arising from limited computational resources including processing time or bandwidth. For local models resulting in convex problems, we derive precise conditions on the local models and their network structure such that our algorithm learns nearly optimal local models. Our analysis reveals an interesting interplay between the convex geometry of local models and the (cluster-) geometry of their network structure.

Networked Federated Learning

Complex numbers define the relationship between entities in many situations. A canonical example would be the off-diagonal terms in a Hamiltonian matrix in quantum physics. Recent years have seen an increasing interest to extend the tools of network science when the weight of edges are complex numbers. Here, we focus on the case when the weight matrix is Hermitian, a reasonable assumption in many applications, and investigate both structural and dynamical properties of the complex-weighted networks. Building on concepts from signed graphs, we introduce a classification of complex-weighted networks based on the notion of structural balance, and illustrate the shared spectral properties within each type. We then apply the results to characterise the dynamics of random walks on complex-weighted networks, where local consensus can be achieved asymptotically when the graph is structurally balanced, while global consensus will be obtained when it is strictly unbalanced. Finally, we explore potential applications of our findings by generalising the notion of cut, and propose an associated spectral clustering algorithm. We also provide further characteristics of the magnetic Laplacian, associating directed networks to complex-weighted ones. The performance of the algorithm is verified on both synthetic and real networks.

Structural Balance and Random Walks on Complex Networks with Complex Weights

This study focuses on long-term forecasting (LTF) on continuous-time dynamic graph networks (CTDGNs), which is important for real-world modeling. Existing CTDGNs are effective for modeling temporal graph data due to their ability to capture complex temporal dependencies but perform poorly on LTF due to the substantial requirement for historical data, which is not practical in most cases. To relieve this problem, a most intuitive way is data augmentation. In this study, we propose \textbf{\underline{U}ncertainty \underline{M}asked \underline{M}ix\underline{U}p (UmmU)}: a plug-and-play module that conducts uncertainty estimation to introduce uncertainty into the embedding of intermediate layer of CTDGNs, and perform masked mixup to further enhance the uncertainty of the embedding to make it generalize to more situations. UmmU can be easily inserted into arbitrary CTDGNs without increasing the number of parameters. We conduct comprehensive experiments on three real-world dynamic graph datasets, the results demonstrate that UmmU can effectively improve the long-term forecasting performance for CTDGNs.

Yu Tian

Papers

Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network

Networked Federated Learning

Structural Balance and Random Walks on Complex Networks with Complex Weights

Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation