NDLI: Reveiling Complexity-Related Time-Series Features with the Monotonic Aggregation Transform

Content Provider	IEEE Xplore Digital Library
Author	Skulimowski, A.M.J.
Copyright Year	2014
Description	Author affiliation: Autom. Control Dept., AGH Univ. of Sci. & Technol., Krakow, Poland (Skulimowski, A.M.J.)
Abstract	Numerous attempts have been made to assess the complexity and predictability of a time series. In AI applications, the latter may be used to determine or classify the origin of an unknown signal. This paper presents the theoretical background to an empirical time series analysis methodology based on the monotonic aggregation transform. For any given time series, its extrema are assumed to contain more information than intermediate data, which is supported empirically for long-memory financial and technological data. Based on this assumption, properties of k-th order minima and maxima are studied as well as their mutual relations. The latter have allowed us to construct a binary decomposition tree and an extremal hull of a time series observation set. It will be proven that the natural characteristic of decomposition trees can be interpreted as an entropy function of the corresponding observation set. Furthermore, the maximum height as well as the sum of all node orders of a decomposition tree is a measure of its information contents. When considered as a function of a sliding time window of constant length in a stationary time series, the above characteristics give us clues as regards the predictability of the original time series, its differences or integrands. We will show the practical implications of the above method in analyzing various kinds of temporal data.
Starting Page	694
Ending Page	700
File Size	934572
Page Count	7
File Format	PDF
ISBN	9781479965724
ISSN	10823409
DOI	10.1109/ICTAI.2014.109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-10
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Vegetation Complexity theory Entropy Transforms Forecasting Finite element analysis forecasting time series monotonic aggregation transform complexity decomposition tree entropy of data sets
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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