GEOMETRIC PERCEPTUAL IMPORTANCE ALGORITHM FOR REAL-TIME VISUALIZATION OF STREAMING TIME SERIES

Source abstract

High-rate data streams require aggregation before screen rendering. The M4 method retainsfourpointswithineachpixelcolumn. However, importantoutliers, sharpbends, andlevelshifts can be lost. This study proposes AdaptiveM4 for improved time-series point selection AdaptiveM4 retains the M4 rasterization structure and changes point selection. A weighted importance score is assigned to candidate points. The score combines deviation, local slope, and local curvature. Each component is normalized within the processed data batch. Outliers and trend reversals receive higher selection priority. An optional domain rule also preserves predefined critical points. The output size is bounded by the specified budget. A time-aware version supports irregularly sampled time series. Ten baseline methods are compared under equal output budgets. Four synthetic datasets and one real labeled dataset are evaluated. Geometric, perceptual, and retention metrics are used for comparison. The highest anomaly-retention F1 reaches 0.318 with tuned weights. The strongest baseline reaches an F1 score of 0.283. However, the real-data F1 decreases to 0.232 under held-out tuning. Therefore, the reported improvement is not confirmed on unseen data. AdaptiveM4 processes 100,000 points in approximately 20 milliseconds. This result indicates substantially lower computational cost than geometric simplifiers. However, whole-curve fidelity remains lower than several baseline methods. The advantage also disappears under a loose matching tolerance. Weight selection remains sensitive to the evaluation dataset. All experiments remain reproducible with fixed random seeds.