A Multivariate Timeseries refers to a sequence of observations or data points collected over time, where each observation consists of multiple variables or features. Instead of having a single variable measured at different points in time, a multivariate time series involves the simultaneous measurement of several variables at each time step. These datasets are prevalent in various fields, and their analysis is crucial for gaining insights, making predictions, and understanding the dynamics of systems over time.


Unsupervised Anomaly Detection Under A Multiple Modeling Strategy Via Model Set Optimization Through Transfer Learning

Unsupervised anomaly detection approaches have been widely accepted in applications for industrial systems. Industrial systems often operate with multiple modes since they work for multiple purposes or under different conditions. In order to deal with the difficulty of anomaly detection due to multiple operating modes, multiple modeling strategies are employed. However, estimating the optimal set of models is a challenging problem due to the lack of supervision and computational burden. In this paper, we propose DeconAnomaly, a deep learning framework to estimate the optimal set of models using transfer learning for unsupervised anomaly detection under a multiple modeling strategy. It reduces computational burden with transfer learning and optimizes the number of models based on a surrogate metric of detection performance. The experimental results show clear advantages of DeconAnomaly.