3 research outputs found
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection
Anomaly detection is a classical but worthwhile problem, and many deep
learning-based anomaly detection algorithms have been proposed, which can
usually achieve better detection results than traditional methods. In view of
reconstruct ability of the model and the calculation of anomaly score, this
paper proposes a time series anomaly detection method based on Variational
AutoEncoder model(VAE) with re-Encoder and Latent Constraint network(VELC). In
order to modify reconstruct ability of the model to prevent it from
reconstructing abnormal samples well, we add a constraint network in the latent
space of the VAE to force it generate new latent variables that are similar
with that of training samples. To be able to calculate anomaly score in two
feature spaces, we train a re-encoder to transform the generated data to a new
latent space. For better handling the time series, we use the LSTM as the
encoder and decoder part of the VAE framework. Experimental results of several
benchmarks show that our method outperforms state-of-the-art anomaly detection
methods.Comment: 13 pages, 3 figure
Anomaly Subsequence Detection with Dynamic Local Density for Time Series
Anomaly subsequence detection is to detect inconsistent data, which always
contains important information, among time series. Due to the high
dimensionality of the time series, traditional anomaly detection often requires
a large time overhead; furthermore, even if the dimensionality reduction
techniques can improve the efficiency, they will lose some information and
suffer from time drift and parameter tuning. In this paper, we propose a new
anomaly subsequence detection with Dynamic Local Density Estimation (DLDE) to
improve the detection effect without losing the trend information by
dynamically dividing the time series using Time Split Tree. In order to avoid
the impact of the hash function and the randomness of dynamic time segments,
ensemble learning is used. Experimental results on different types of data sets
verify that the proposed model outperforms the state-of-art methods, and the
accuracy has big improvement
A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data
collection, enabling a large amount of data to be gathered over time and thus
generating time series. Mining this data has become an important task for
researchers and practitioners in the past few years, including the detection of
outliers or anomalies that may represent errors or events of interest. This
review aims to provide a structured and comprehensive state-of-the-art on
outlier detection techniques in the context of time series. To this end, a
taxonomy is presented based on the main aspects that characterize an outlier
detection technique.Comment: 32 pages, 21 figures, submitted to ACM Computing Surveys (CSUR