Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Optimal multi-scale patterns in time series streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
StreamKrimp: Detecting Change in Data Streams
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Traffic events modeling for structural health monitoring
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
InfraWatch: data management of large systems for monitoring infrastructural performance
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Mining characteristic multi-scale motifs in sensor-based time series
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The behavior of many complex physical systems is affected by a variety of phenomena occurring at different temporal scales. Time series data produced by measuring properties of such systems often mirrors this fact by appearing as a composition of signals across different time scales. When the final goal of the analysis is to model the individual phenomena affecting a system, it is crucial to be able to recognize the right temporal scales and to separate the individual components of the data. In this paper, we approach this challenge through a combination of the Minimum Description Length (MDL) principle, feature selection strategies, and convolution techniques from the signal processing field. As a result, our algorithm produces a good decomposition of a given time series and, as a side effect, builds a compact representation of its identified components. Experiments demonstrate that our method manages to identify correctly both the number and the temporal scale of the components for real-world as well as artificial data and show the usefulness of our method as an exploratory tool for analyzing time series data.