IEEE

Coupled Attention Networks for Multivariate Time Series Anomaly Detection

Abstract: Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing ...
IEEE

Multivariate Time Series Forecasting Using Multiscale Recurrent Networks With Scale Attention and Cross-Scale Guidance

Abstract: Multivariate time series (MTS) forecasting is considered as a challenging task due to complex and nonlinear interdependencies between time steps and series. With the advance of deep learning ...
GitHub

FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

If you find this repo useful, please cite our paper. @inproceedings{yi2023fouriergnn, title={Fourier{GNN}: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective}, author={Kun ...
GitHub

Partial Derivatives and Gradients

Partial Derivatives and Gradients "The gradient is the generalization of the derivative to functions of several variables, and it points in the direction in which the function increases most rapidly.
Frontiers

Combining machine learning and physiological network models for sepsis prediction

Self-Organization of Complex Physiological Networks: Synergetic Principles and Applications — In Memory of Hermann Haken ...