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Visualization and analysis of probability distributions of large temporal data

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thesis
posted on 04.03.2022, 03:32 authored by SAYANI GUPTA
Increasingly, 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

Australia

Principal supervisor

Rob Hyndman

Additional supervisor 1

Dianne Cook

Year of Award

2022

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics