The relationship between predictability and reconstructability, and how it can vary in opposite directions in complex systems. The work is based on information theory and was performed on various dynamics on random graphs, including continuous deterministic systems, and provides analytical calculations of the uncertainty coefficients for many different systems.
The article discusses the challenges faced in evaluating anomaly detection in time series data and introduces Proximity-Aware Time series anomaly Evaluation (PATE) as a solution. PATE provides a weighted version of Precision and Recall curve and considers temporal correlations and buffer zones for a more accurate and nuanced evaluation.
PySpark for time-series data, discussing data ingestion, extraction, and visualization with practical implementation code.
