Aug 2026· 2026 6th International Conference on Computer Science and Blockchain (CCSB)· pp. 574-580· 0 citations· 15 references
Abstract
Long-horizon forecasting is important for applications such as electricity-load management and climate analysis, but a model must represent both distant temporal interactions and local periodic variations to perform well. We present RDDPformer, a two-path Transformer that processes temporal patterns with dilated convolutions and spectral patterns with Fourier decomposition. Instead of converting the spectrum to magnitude and phase, the proposed model feeds its real and imaginary components, obtained through the DFT, into separate frequency encoders. A convolution-enhanced pyramid attention module extracts local periodic structure, while cross-attention selects frequency information conditioned on temporal features. RevIN is applied to reduce the influence of distribution changes. On ETTh1, ETTh2, ETTm1, ETTm2, and Weather, RDDPformer lowers the MSE relative to Pyraformer by 42.7%–89.9% across the evaluated horizons. The ablation results further show that both the real/imaginary representation and the cross-attention module make complementary contributions.
Photovoltaic (PV) power day-ahead forecasting is crucial for grid dispatch and energy management, yet its accuracy is severely challenged by the nonstationarity and multiscale periodicity of the power series. To address the limitations of existing transformer models in explicit periodic modeling and handling nonstati...
Yan-Guo Huang, Zhen-Yu Zhong, Wei-Feng Liu et al.· Journal of Energy Engineerin...· 0 citations
Long-horizon time series forecasting is commonly improved through increasingly specialized neural architectures, making the resulting advances difficult to transfer across forecasting models. We instead propose an architecture-independent representation that combines component-wise forecasting with selective spectral a...
Kyrylo Yemets, Ivan Shkvir· Scientific Reports· 0 citations
The findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments.
FreqNet is presented, a compact and interpretable forecaster that operates in the frequency domain that achieves the best average error on ETTh2, Exchange, and NASDAQ 100, is competitive across the remaining ETT datasets and the high-dimensional sets, and improves on the closely related frequency-domain baseline FITS.
SETTer is introduced, a transformer-based model that addresses challenges of long-term multivariate time series forecasting by incorporating novel techniques for decoupled self-attention and hybrid masking and outperforms state-of-the-art models in 88% of the scenarios.
Abraham Ezema, C. Eze, F. Ponci et al.· 0 citations
Time series analysis is fundamental in domains such as finance, healthcare, and meteorology. Real-world time series often exhibit multiscale characteristics shaped by diverse latent factors, resulting in intricate temporal patterns and rich frequency structures. However, existing approaches typically focus on either fr...
Xiao-Han Jiang, Jing-Yuan Wang, Jia-Hao Ji et al.· IEEE Transactions on Knowled...· 0 citations
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