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 of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential.Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification.ADSiamNet effectively identifies localized patterns in opi the color that keeps on giving time-series data and smooths detected anomalies using a quantile-based technique.
In tests with physical activity data from Actigraph watches klaire labs ashwagandha and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods.The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns.Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies.
Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability.Future research will focus on multivariate time-series datasets.