Causal and Local Correlations Based Network for Multivariate Time Series Classification
Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods.
Code (1)
Tasks
Graph Neural NetworkTime SeriesTime Series ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis
In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series. Causal patterns refer to the signal patterns withi…
Time SeriesTime Series AnalysisCluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series
Early and accurate detection of anomalies in time series data is critical, given the significant risks associated with false or missed detections. While MLP-based mixer models have shown promise in time series analysis, …
Anomaly DetectionTime SeriesTime Series AnalysisTime Series Anomaly DetectionA Pattern Discovery Approach to Multivariate Time Series Forecasting
Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multip…
DiversityMultivariate Time Series ForecastingTime SeriesTime Series Analysis+1Causal Discovery with Inverted Self-attention for Multivariate Time Series
Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods often struggle to capture these complexities…
Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of caus…
Graph Representation LearningTime Series AnalysisAnomaly Detection