Distance-based outliers in sequences
GK Palshikar - … , ICDCIT 2005, Bhubaneswar, India, December 22-24 …, 2005 - Springer
Distributed Computing and Internet Technology: Second International Conference …, 2005•Springer
Automatically finding interesting, novel or surprising patterns in time series data is useful in
several applications, such as fault diagnosis and fraud detection. In this paper, we extend
the notion of distance-based outliers to time series data and propose two algorithms to
detect both global and local outliers in time series data. We illustrate these algorithms on
some real datasets. Keywords: Novelty detection, Outlier detection, Time series, Sequence
mining.
several applications, such as fault diagnosis and fraud detection. In this paper, we extend
the notion of distance-based outliers to time series data and propose two algorithms to
detect both global and local outliers in time series data. We illustrate these algorithms on
some real datasets. Keywords: Novelty detection, Outlier detection, Time series, Sequence
mining.
Abstract
Automatically finding interesting, novel or surprising patterns in time series data is useful in several applications, such as fault diagnosis and fraud detection. In this paper, we extend the notion of distance-based outliers to time series data and propose two algorithms to detect both global and local outliers in time series data. We illustrate these algorithms on some real datasets.
Keywords: Novelty detection, Outlier detection, Time series, Sequence mining.
Springer
