Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, do this in a way that captures complex inter-sensor relationships, detects explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements anomaly detection datasets; however, existing meth...

A method for detecting outlier samples in a multivariate time series dataset is proposed. It assumed that an outlying characterized by having been generated from different process than those associated with the rest of series. Each described means estimator its quantile cross-spectral density, which treated as functional datum. Then score assigned to each using depths. broad simulation study sh...

Multivariate Time series data play important roles in our daily life. How to use these the process of prediction is a highly attractive study for many researchers. To achieve this goal, paper, we present novel multivariate time method based on multi-attention generative adversarial network. This includes three phases explore prediction. Firstly, encoder stage consists two modules, from which in...

We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists two steps. First, set features based on quantile cross-spectral density and maximum overlap discrete wavelet transform are extracted from each series...

Although various feature extraction algorithms have been developed for time series data, it is still challenging to obtain a flat vector representation with incorporating both of time-wise and variable-wise association between multiple series. Here we develop an algorithm, called Unsupervised Feature Extraction using Kernel Stacking (UFEKS), that constructs in unsupervised manner. UFEKS kernel ...