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View article: Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling Open
With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural n…
View article: Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks.
Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks. Open
Multivariate time series (MTS) forecasting is an important problem in many fields. Accurate forecasting results can effectively help decision-making. Variables in MTS have rich relations among each other and the value of each variable in M…
View article: MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting Open
Multivariate time series forecasting, which analyzes historical time series to predict future trends, can effectively help decision-making. Complex relations among variables in MTS, including static, dynamic, predictable, and latent relati…
View article: Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting Open
Multivariate time series forecasting is widely used in various fields. Reasonable prediction results can assist people in planning and decision-making, generate benefits and avoid risks. Normally, there are two characteristics of time seri…