Anomaly detection in hydrological network time series via multiresolution analysis

The territorial extension of Brazil imposes logistic challenges on hydrologic monitoring and increases the chances of systematic observational errors, gauge displacements, downsampling during observation, and low density of streamflow samples on high water stage. Consequently, time series would exhibit artifacts such as gaps, highfrequency anomalies, inhomogeneity, and trends. An innovative anomaly detection system based on multiresolution analysis (MADE) was developed and used to detect synthetic anomalies in raw water stage time series. MADE uses both time and frequency as contextual attributes to identify anomalies. The balance between temporal and frequency resolution allows for capturing low-frequency coarse information and high-frequency details. On MADE, Generalized Extreme Studentized Deviate (GESD) detected short time anomalies on water stage wavelet faster modes (2-, 4-, and 8-days periods). For periods greater than 16 days, Wavelet Coherence and Multiple Wavelet Coherence detected similar water stage time series anomalies. MADE outperformed a Long Short-Term Memory model, a time regression neural network k-Nearest Neighbor model and an Empirical Data Analytics model coupled with clustering techniques on detecting both fast and slow time-scales synthetic anomalies introduced in a Sao Francisco River water stage time series, using time and frequency domain to pursuit point, collective and contextual anomalies.