Temporal data mining using hidden periodicity analysis

Weiqiang Lin, Mehmet Orgun

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Data mining, often called knowledge discovery in databases (KDD), aims at semiautomatic tools for the analysis of large data sets. This report is first intended to serve as a timely overview of a rapidly emerging area of research, called temporal data mining (that is, data mining from temporal databases and/or discrete time series). We in particular provide a general overview of temporal data mining, motivating the importance of problems in this area, which include formulations of the basic categories of temporal data mining methods, models, techniques and some other related areas. This report also outlines a general framework for analysing discrete time series databases, based on hidden periodicity analysis, and presents the preliminary results of our experiments on the exchange rate data between US dollar and Canadian dollar.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science,
Subtitle of host publicationVolume 1932 : Lecture notes in artificial intelligence
EditorsZbigniew W. Ras, Setsue Ohsuga
Place of PublicationHeidelberg, Germany
PublisherSpringer, Springer Nature
Pages49-58
Number of pages10
Volume1932
ISBN (Print)3540410945
DOIs
Publication statusPublished - 2000
EventThe Twelfth International Symposium on Methodologies for Intelligent Systems - ISMIS 2000 - Charlotte, USA
Duration: 11 Oct 200014 Oct 2000

Conference

ConferenceThe Twelfth International Symposium on Methodologies for Intelligent Systems - ISMIS 2000
CityCharlotte, USA
Period11/10/0014/10/00

Fingerprint Dive into the research topics of 'Temporal data mining using hidden periodicity analysis'. Together they form a unique fingerprint.

Cite this