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Download fileVisualization and analysis of probability distributions of large temporal data
thesis
posted on 2022-03-04, 03:32 authored by SAYANI GUPTAIncreasingly, data is recorded at much finer temporal scales. However, data collected at an hourly scale can also be analyzed using coarser scales such as days, months or quarters. Cyclic granularities representing repetitions in time (such as hour-of-the-day, day-of-the-week, work-day/weekend) are effective for analyzing repetitive patterns in time series data. To fully comprehend these patterns, one must traverse all cyclic granularities. This is difficult with many options but only few of them revealing major patterns. This thesis presents methods for screening the interesting ones and then visualizing the distributions to support the discovery of regular patterns and clusters of behaviours.
History
Campus location
AustraliaPrincipal supervisor
Rob HyndmanAdditional supervisor 1
Dianne CookYear of Award
2022Department, School or Centre
Econometrics and Business StatisticsCourse
Doctor of PhilosophyDegree Type
DOCTORATEFaculty
Faculty of Business and EconomicsUsage metrics
Keywords
data visualizationcyclic granularitiesperiodicitiespermutation testsdistributional differenceJensen-Shannon distancessmart meter datastatistical distributionscalendar algebragrammar of graphicsclusteringtime granularitiesTime seriesprobability distributionselectricity consumption behaviorR softwareR packageStatisticsApplied Statistics