Sampled Time Series

Sampled time series analysis focuses on extracting meaningful information and building predictive models from data points collected at irregular intervals, a common characteristic of real-world datasets. Current research emphasizes developing advanced models, such as neural controlled differential equations (NCDEs) and transformer architectures, to handle the challenges posed by missing data and non-uniform sampling, often incorporating techniques like imputation, continuous-time representations, and attention mechanisms. These advancements are crucial for improving accuracy in diverse applications, including forecasting, anomaly detection, and state estimation in areas like robotics and environmental monitoring. The ability to effectively analyze irregularly sampled data unlocks insights from a wider range of complex systems.