DS-SpecIT: Decomposed Spectral Inverted Transformer for Interference-Aware Spectrum Forecasting

Weformer: Adaptive spectral cross-variable transformer for long-horizon weather forecasting

Accurate long-term weather forecasting from multivariate sensor time series remains challenging due to the inherent non-stationarity of atmospheric observations acquired by heterogeneous environmental sensors and the complex physical coupling among meteorological variables. Existing general-purpose deep forecasting models, while successful on idealized benchmarks, suffer notable performance degradation on raw ground-station sensor data because they lack domain-specific mechanisms for cross-variable interaction and noise resistance. We propose Weformer, a Transformer-based architecture purpose-built for sensor-derived weather time series. Weformer introduces two core innovations: (1) a Frequency-driven Cross-Variable Rotary Position Embedding (CrossVarRoPE) that extracts dominant spectral patterns via the Fourier transform and injects adaptive, cross-variable-aware positional encoding into the attention mechanism, and (2) a Global Token mechanism that distills macroscopic environmental context through cross-attention, preventing overfitting to local high-frequency sensor noise. We provide rigorous theoretical analysis, including a noise-suppression bound, a capacity bound for the global token, and a forecasting error decomposition, to justify the design. To bridge the evaluation gap in current benchmarks, we curate seven new real-world datasets sourced from ground-station sensor networks and energy-grid monitoring systems, spanning diverse climate zones and temporal resolutions. Extensive experiments on eight datasets demonstrate that Weformer achieves the best overall performance among seven competitive baselines—the lowest global average MSE and MAE and the largest number of first-place results—under both standard and long-horizon settings (up to 2,880 steps, i.e., 20–120 days depending on the sampling resolution), while CrossVarRoPE serves as a portable plug-in that improves standard Transformer architectures in most evaluated settings. Code and data are available at https://github.com/sekiro1211/Weformer.

Nonstationarity-Aware IoT Time-Series Modeling for Industrial Fault Detection and Diagnosis

An advanced neural network architecture that dynamically adapts to distribution shifts through a continuous domain adaptation mechanism and an adaptive attention module is proposed that significantly outperforms existing state-of-the-art diagnostic models in terms of accuracy, robustness, and generalization capabilities under severe operational transitions.

MFSNet: lightweight multi-scale MLP-guided frequency suppression network for multivariate time series forecasting

A dual-path multi-view framework for deployment-oriented time series anomaly detection

CoFFormer: A Collaborative Frequency-Domain-Enhanced Network for Sustainable Wind Power Forecasting Under Non-Stationary Conditions

Accurate wind power forecasting is essential for renewable-energy accommodation, low-carbon dispatch, and the sustainable operation of modern power systems. However, wind power series exhibit pronounced non-stationarity, strong volatility, and multi-scale evolution, making long-term trends and short-term disturbances difficult to characterize jointly. In addition, multi-step forecasting errors tend to accumulate with increasing horizons, degrading model accuracy and stability. To address these issues, this study proposes CoFFormer, a collaborative frequency-domain-enhanced network for non-stationary wind power forecasting. The model reduces input modeling complexity, strengthens collaborative representation of heterogeneous temporal information, and suppresses output-stage error accumulation. Specifically, embedded series decomposition mitigates coupling interference between trend and fluctuation components. Differentiated temporal modeling and dynamic gating then adaptively coordinate the contributions of different feature representations, while frequency-domain residual compensation enhances the recovery of periodic structures and local oscillations. Experiments on ETTh2, wind_speed, WindPower, and Location2 demonstrate strong competitiveness across forecasting horizons. CoFFormer achieves MSE/MAE values of 0.0957/0.2238 and 0.1508/0.2889 on ETTh2 for 12- and 24-step forecasting, and 0.0617/0.1490 and 0.3838/0.3948 on WindPower for 3- and 24-step forecasting, outperforming most baselines. Ablation studies confirm the effectiveness and synergy of each component, providing an effective solution for high-accuracy multi-step forecasting of complex non-stationary wind power series.

A Time–Frequency Multi-Task Framework for Network Monitoring and Anomaly Detection

Networked systems continuously generate heterogeneous time series, including Key Performance Indicator (KPI) streams, logs, and spectrum measurements, whose interpretation is essential for automated monitoring, diagnosis, and control. Existing analysis approaches either rely heavily on labeled data specific to each deployment or fail to capture joint time-domain and frequency-domain characteristics that are common in communication signals. Motivated by these limitations for cognitive communications and network monitoring, we propose the Time-Frequency Multi-Task Network (TFMTNet), a self-supervised framework that provides a transferable representation module for the evaluated network telemetry tasks. TFMTNet integrates a multi-scale time-frequency fusion backbone with three complementary pretraining objectives and provides lightweight task heads for anomaly detection, forecasting, and classification. Under a pretraining and adaptation protocol, the model is pretrained once and then adapted to target domains with limited labeled data. Empirical evaluation on ten anomaly detection datasets and additional public classification and forecasting datasets, including an Artificial Intelligence for IT Operations (AIOps) telemetry dataset, shows consistent cross-domain improvements under the evaluated settings. For cognitive communications, TFMTNet learns representations that can feed downstream reasoning and control modules, including Software-Defined Networking (SDN) decision making, spectrum management, and AIOps pipelines, thereby supporting the perception, reasoning, and control loop in networked systems.