Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting performance by modeling each channel individually, but it often suffers from poor generalization and overlooks meaningful inter-channel interactions. Conversely, Channel-Dependent (CD) strategies aggregate all channels, which may introduce irrelevant information and lead to oversmoothing. Despite recent progress, few existing methods offer the flexibility to adaptively balance CI and CD strategies in response to varying channel dependencies. To address this, we propose a generic plugin xCPD, that can adaptively model the channel-patch dependencies from the perspective of graph spectral decomposition. Specifically, xCPD first projects multivariate signals into the frequency domain using a shared graph Fourier basis, and groups patches into low-, mid-, and high-frequency bands based on their spectral energy responses. xCPD then applies a channel-adaptive routing mechanism that dynamically adjusts the degree of inter-channel interaction for each patch, enabling selective activation of frequency-specific experts. This facilitates fine-grained input-aware modeling of smooth trends, local fluctuations, and abrupt transitions. xCPD can be seamlessly integrated on top of existing CI and CD forecasting models, consistently enhancing both accuracy and generalization across benchmarks. The code is available https://github.com/Clearloveyuan/xCPD.
Code (0)
등록된 구현이 없습니다.
Tasks
Time Series ForecastingSimilar Papers 제목 키워드 기반
Task-Aware Mixture-of-Experts for Time Series Analysis
Time Series Analysis is widely used in various real-world applications such as weather forecasting, financial fraud detection, imputation for missing data in IoT systems, and classification for action recognization. Mixt…
Time Series AnalysisWeather ForecastingFraud DetectionSentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting
Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architectures exploit either temporal or inter-chann…
DecoderMultivariate Time Series ForecastingTime SeriesTime Series ForecastingSensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting
Among the existing Transformer-based multivariate time series forecasting methods, iTransformer, which treats each variable sequence as a token and only explicitly extracts cross-variable dependencies, and PatchTST, whic…
AttributeMultivariate Time Series ForecastingTime SeriesTime Series ForecastingTiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit divers…
Multivariate Time Series ForecastingEnhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding
Transformers have recently gained popularity in time series forecasting due to their ability to capture long-term dependencies. However, many existing models focus only on capturing temporal dependencies while omitting i…
Time SeriesTime Series Forecasting