Irregular Time Series

Irregular time series, characterized by unevenly spaced data points, pose significant challenges for traditional time series analysis methods. Current research focuses on developing novel model architectures, such as continuous-time transformers, neural stochastic differential equations, and continuous recurrent units, to effectively capture the complex temporal dynamics inherent in these data. These advancements aim to improve forecasting accuracy, anomaly detection, and causal inference in diverse applications, including healthcare, finance, and environmental monitoring, where irregularly sampled data are prevalent. The ultimate goal is to create robust and efficient methods that fully leverage the information contained within both the data values and their irregular timing.