# A unifying framework for detecting outliers and change points from non-stationary time series data

@article{Yamanishi2002AUF, title={A unifying framework for detecting outliers and change points from non-stationary time series data}, author={Kenji Yamanishi and Jun’ichi Takeuchi}, journal={Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining}, year={2002} }

We are concerned with the issues of outlier detection and change point detection from a data stream. [] Key Method In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm, which can track the changing data source adaptively by forgetting the effect of past data gradually.

## 303 Citations

### A unifying framework for detecting outliers and change points from time series

- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2006

This paper presents a unifying framework for dealing with outlier detection and change point detection, which is incrementally learned using an online discounting learning algorithm and compared with conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security.

### Coding of Non-Stationary Sources as a Foundation for Detecting Change Points and Outliers in Binary Time-Series

- Computer ScienceAusDM
- 2012

Theoretical foundations are provided for the use of adaptive estimation procedures for estimating and adapting distributions in real-time for non-stationary data using an adaptive version of the Context Tree Weighting compression algorithm.

### Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data

- Computer ScienceIJCAI
- 2013

A supervised learning based change-point detection approach in which the separability of past and future data at time t is used as plausibility of change-points and a detection measure called the additive Hilbert-Schmidt Independence Criterion (aHSIC) is proposed.

### Online Conditional Outlier Detection in Nonstationary Time Series

- Computer ScienceFLAIRS
- 2017

This work proposes a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation.

### A Novel Framework for Context-aware Outlier Detection in Big Data Streams

- Computer ScienceJournal of Digital Information Management
- 2018

This paper proposes a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier Detection rather than using the streamcontent alone or applying the contextual detection on content anomalies only.

### Outlier Detection for Temporal Data

- Computer ScienceOutlier Detection for Temporal Data
- 2014

This book presents a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers, and lists down a taxonomy of proposed techniques for temporalOutlier detection.

### Incremental Local Outlier Detection for Data Streams

- Computer Science2007 IEEE Symposium on Computational Intelligence and Data Mining
- 2007

The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points in the data set.

### Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation

- Computer Science, MathematicsSDM
- 2009

This paper provides a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner and allows for non-parametric density estimation, which is known to be a difficult problem.

### Efficient and flexible algorithms for monitoring distance-based outliers over data streams

- Computer ScienceInf. Syst.
- 2016

### Anomaly Detection over Concept Drifting Data Streams

- Computer Science
- 2010

A hybrid framework by combining LOF (Local outlier Factor) and BPNN (Back propagation Neural Network), appropriate for detecting outliers in data streams, is proposed and provides equivalent detection performance as the iterated static LOF algorithm, while requiring significantly less computational time.

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