An Anomaly Analysis Method Based on Morphological Features

The basic method and concept of time series feature extraction is applied to anomaly analysis of seismic data. A new method for piecewise representation based on morphological feature points and a new feature extraction method are proposed. The experimental results show that the proposed piecewise representation method can achieve smaller fitting error and retain the morphological features of the original data better than existing approaches when applied to three real world datasets. The experimental results also illustrate that the proposed method for feature extraction can extract changes in electromagnetic data and distinguish the extent of abnormal changes in real world datasets. In one case, the results show that the approach could identify change features that might be related to an earthquake.

[1]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[2]  Eugene Fink,et al.  Search for Patterns in Compressed Time Series , 2002, Int. J. Image Graph..

[3]  Shaochun Wu,et al.  A Platform for Parallel Data Mining on Cluster System , 2005 .

[4]  Matthias Holschneider,et al.  From Alarm‐Based to Rate‐Based Earthquake Forecast Models , 2012 .

[5]  Weimin Li,et al.  A Novel Load Shedding Framework LS-LG for Similarity Querying on Data Stream , 2009, 2009 WRI World Congress on Software Engineering.

[6]  Ben Shneiderman,et al.  Querying event sequences by exact match or similarity search: Design and empirical evaluation , 2012, Interact. Comput..

[7]  Haixun Wang,et al.  Landmarks: a new model for similarity-based pattern querying in time series databases , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[8]  Emrah Dogan,et al.  An unsupervised learning algorithm: application to the discrimination of seismic events and quarry blasts in the vicinity of Istanbul , 2011 .

[9]  Silvia Scarpetta,et al.  Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano , 2009, WIRN.

[10]  Kenji Yamanishi,et al.  A unifying framework for detecting outliers and change points from time series , 2006, IEEE Transactions on Knowledge and Data Engineering.

[11]  Harry Wechsler,et al.  A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Carlo Zaniolo,et al.  An adaptive learning approach for noisy data streams , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[13]  Yu Zhao-chun.  An Improved Sequential Pattern Mining Algorithm and Its Application in Seismology , 2005 .

[14]  Yaxin Bi,et al.  A Geometric Moving Average Martingale method for detecting changes in data streams , 2012, SGAI Conf..

[15]  Shengli Wu,et al.  A Comparative Analysis for Detecting Seismic Anomalies in Data Sequences of Outgoing Longwave Radiation , 2009, KSEM.

[16]  Menas Kafatos,et al.  An early warning system for coastal earthquakes , 2006 .

[17]  Marcus A. Maloof,et al.  Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.

[18]  Wang Wei,et al.  A Time-Sequence Similarity Matching Algorithm for Seismological Relevant Zones , 2006 .

[19]  Christos Faloutsos,et al.  Fast Time Sequence Indexing for Arbitrary Lp Norms , 2000, VLDB.

[20]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[21]  Sriram Padmanabhan,et al.  Prefix-querying: an approach for effective subsequence matching under time warping in sequence databases , 2001, CIKM '01.

[22]  Ludmila I. Kuncheva,et al.  Change Detection in Streaming Multivariate Data Using Likelihood Detectors , 2013, IEEE Transactions on Knowledge and Data Engineering.

[23]  Sanjay Ranka,et al.  Statistical change detection for multi-dimensional data , 2007, KDD '07.