Non-Parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis

Signal processing based filtering methods. Those approaches Data sets collected from a wide variety of research disciplines, implicitly assume that the "normal" component including computer science, economic, biology and of the time series has a sparse representation in the frequency social science, are in the form of multiple co-evolving time or wavelet domain. Hence the outlier detection problem is reduced series. In this work, we consider the task of outlier (or novelty) to a spectral analysis using low pass or band pass filters, detection given the aforementioned data type. The core or is solved by denoising/signal reconstruction using spectral difficulty, however, is to integrate both the temporal dependence or wavelet techniques (Mallat 2008). It is worth pointing out and the interactions among correlated time series for that the signal-processing-based methods have close ties with overall modeling and learning.