2012 | OriginalPaper | Chapter
MDL-Based Analysis of Time Series at Multiple Time-Scales
Authors : Ugo Vespier, Arno Knobbe, Siegfried Nijssen, Joaquin Vanschoren
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.