Prominent Discord Discovery with Matrix Profile Application to Climate Data Insights

Описание: Prominent Discord Discovery with Matrix Profile : Application to Climate Data Insights

Authors

Hussein El Khansa, Carmen Gervet and Audrey Brouillet, Univ. La Réunion, France

Abstract

The definition and extraction of actionable anomalous discords, i.e. pattern outliers, is a challenging problem in data analysis. It raises the crucial issue of identifying criteria that would render a discord more insightful than another one. In this paper, we propose an approach to address this by introducing the concept of prominent discord. The core idea behind this new concept is to identify dependencies among discords of varying lengths. How can we identify a discord that would be prominent? We propose an ordering relation, that ranks discords and we seek a set of prominent discords with respect to this ordering. Our contributions are 1) a formal definition, ordering relation and methods to derive prominent discords based on Matrix Profile techniques, and 2) their evaluation over large contextual climate data, covering 110 years of monthly data. The approach is generic and its pertinence shown over historical climate data.

Keywords

Prominent discord discovery, Large time series, Matrix profile, Climate data.

Full Text : https://aircconline.com/csit/papers/v...
Abstract URL : https://aircconline.com/csit/abstract...
Volume URL : https://airccse.org/csit/V12N08.html

#Prominentdiscorddiscovery #Largetimeseries #Matrixprofile #Climatedata

00:00 Objective
01:55 Introduction
01:15 Climate Extremes
01:55 Machine Learning
02:25 Outlier Types
03:20 Goal
03:50 Matrix Profile
05:40 Main Challenge
08:20 Our Method
10:46 Results
12:31 Comparison Result
13:36 Future Work