Core Concepts
The author proposes FCM-wDTW, an unsupervised distance metric learning method for anomaly detection over multivariate time series. By encoding raw data into a latent space and utilizing fuzzy C-means clustering with locally weighted DTW, anomalies can be efficiently identified through data reconstruction.
Abstract
The content introduces FCM-wDTW, an innovative approach to unsupervised anomaly detection in multivariate time series. The method encodes data into a latent space using fuzzy C-means clustering and locally weighted DTW, demonstrating competitive accuracy and efficiency across various benchmarks. The proposed algorithm optimizes the objective function through closed-form solutions and efficient optimization techniques. Experiments show superior performance compared to existing methods in terms of both AUC-ROC and AUC-PR metrics.
Stats
Distance-based time series anomaly detection methods are prevalent due to their relative non-parametric nature.
Euclidean distance is commonly used but sensitive to noise.
FCM-wDTW introduces locally weighted DTW into fuzzy C-means clustering.
Experiments with 11 benchmarks demonstrate competitive accuracy and efficiency.
Quotes
"Anomalies are identified by reconstructing data from the latent space."
"Our contributions include improving FCM clustering with wDTW and proposing a multivariate anomaly detection method."
"FCM-wDTW outperforms all other methods on four datasets in terms of both AUC-ROC and AUC-PR."