Inter Series

Inter-series analysis focuses on modeling the complex relationships between multiple time series, aiming to improve forecasting accuracy and understanding of dynamic systems. Current research emphasizes developing deep learning models, particularly transformer-based architectures and graph neural networks, to effectively capture both intra-series (within a single time series) and inter-series (between multiple time series) dependencies, often incorporating techniques like attention mechanisms and multi-scale decompositions. This work is significant because accurate multivariate time series forecasting has broad applications across various fields, including finance, healthcare, and environmental monitoring, where understanding inter-series dynamics is crucial for better decision-making. Improved modeling techniques are leading to more accurate predictions and a deeper understanding of complex interconnected systems.