Neural Granger

Neural Granger causality leverages deep learning to identify causal relationships between variables in time series data, going beyond traditional Granger causality's limitations with complex, nonlinear systems. Current research focuses on improving model accuracy and interpretability through architectures like Variational Autoencoders (VAEs) and novel regularization techniques such as Jacobian regularizers, addressing challenges in handling high-dimensional data and uncovering fine-grained causal structures. These advancements enable applications across diverse fields, including climate forecasting, energy market analysis, and root cause analysis in complex systems like microservices, offering more accurate predictions and insightful causal explanations.