Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making.Anomalies can compromise data quality and operational efficiency.The complexity weboost splitter of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection