Temporal data mining using shape space representations of time series

Authors:
Erich Fuchs;Thiemo Gruber;Helmuth Pree;Bernhard Sick
Affiliations:
Institute for Software Systems in Technical Applications, University of Passau, Germany;Computationally Intelligent Systems Group, Department of Informatics and Mathematics, University of Passau, Germany;Computationally Intelligent Systems Group, Department of Informatics and Mathematics, University of Passau, Germany;Computationally Intelligent Systems Group, Department of Informatics and Mathematics, University of Passau, Germany
Venue:
Neurocomputing
Year:
2010

Citing 51
Cited 4

The fast Fourier transform and its applications

The fast Fourier transform and its applications
R-trees: a dynamic index structure for spatial searching

Readings in database systems
The R*-tree: an efficient and robust access method for points and rectangles

SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Updating and downdating of orthogonal polynomials with data fitting applications

SIAM Journal on Matrix Analysis and Applications
Ten lectures on wavelets

Ten lectures on wavelets
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
On the approximation of curves by line segments using dynamic programming

Communications of the ACM
Locally adaptive dimensionality reduction for indexing large time series databases

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Feature Extraction, Construction and Selection: A Data Mining Perspective

Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A Survey of Temporal Knowledge Discovery Paradigms and Methods

IEEE Transactions on Knowledge and Data Engineering
An Online Algorithm for Segmenting Time Series

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects

VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases

PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A Framework for Robust Subspace Learning

International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration

Data Mining and Knowledge Discovery
Local feature extraction and its applications using a library of bases

Local feature extraction and its applications using a library of bases
Supervised classification with temporal data

Supervised classification with temporal data
Generalized feature extraction for structural pattern recognition in time-series data

Generalized feature extraction for structural pattern recognition in time-series data
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Probabilistic discovery of time series motifs

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Amnesic Approximation of Streaming Time Series

ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

Machine Learning
Pattern Discovery of Fuzzy Time Series for Financial Prediction

IEEE Transactions on Knowledge and Data Engineering
YALE: rapid prototyping for complex data mining tasks

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
A Novel Scalable Algorithm for Supervised Subspace Learning

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis

IEEE Transactions on Knowledge and Data Engineering
Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection

Neurocomputing
Mining Loosely Structured Motifs from Biological Data

IEEE Transactions on Knowledge and Data Engineering
Two-dimensional subspace classifiers for face recognition

Neurocomputing
Letters: Orthogonal discriminant linear local tangent space alignment for face recognition

Neurocomputing
Letters: Neighborhood discriminant tensor mapping

Neurocomputing
Machine Learning and Data Mining: Introduction to Principles and Algorithms

Machine Learning and Data Mining: Introduction to Principles and Algorithms
Scene segmentation based on IPCA for visual surveillance

Neurocomputing
On-line motif detection in time series with SwiftMotif

Pattern Recognition
Multi-view face recognition based on tensor subspace analysis and view manifold modeling

Neurocomputing
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Ubiquitously supervised subspace learning

IEEE Transactions on Image Processing
Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks

IEEE Transactions on Neural Networks
Discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle

MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations

IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Online signature verification with new time series kernels for support vector machines

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Preventing meaningless stock time series pattern discovery by changing perceptually important point detection

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A Parameter-Free Framework for General Supervised Subspace Learning

IEEE Transactions on Information Forensics and Security
Human Age Estimation With Regression on Discriminative Aging Manifold

IEEE Transactions on Multimedia
Takagi--Sugeno--Kang Fuzzy Classifiers for a Special Class of Time-Varying Systems

IEEE Transactions on Fuzzy Systems

Time-series data mining

ACM Computing Surveys (CSUR)
Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

Neurocomputing
Asynchronism-based principal component analysis for time series data mining

Expert Systems with Applications: An International Journal
A methodological approach to mining and simulating data in complex information systems

Intelligent Data Analysis

Quantified Score

Hi-index	0.01

Visualization

Abstract

Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.