Highly comparative time-series analysis

Summary

This research involves developing new methods for time-series analysis based on a new analytic framework for understanding structure in time series.

Supervisor(s)

Dr Ben Fulcher

Research Location

School of Physics

Program Type

Masters/PHD

Synopsis

The world is constantly changing around us: from the fluctuations of the wind on our faces to the characteristic pulsing of our own heart-beat. How do we capture interesting patterns in these data that allow us to make useful predictions: What should we measure about heart beat dynamics to decide whether a subject is at risk of heart failure? What should we measure about economic data to predict whether the economy will grow or is at risk of collapse? What should we measure about a set of credit-card transactions to predict whether fraud is likely to be occurring? We have recently developed a unified machine-learning framework, hctsa, for leveraging thousands of scientific time-series analysis methods to partially automate solving problems of this type. The framework has seen wide success on a range of scientific problems, but much remains to be done! A broad range of projects exist and can be tailored to the specific interests of the student, including: (1) theoretical studies of criticality and other types of nonlinear dynamics, (2) solving specific real-world problems through data analysis via collaborations with clinicians and other scientists; and (3) properly calibrating our time-series feature library by developing simple empirical tests.

Additional Information

Excellent facilities are available to carry out all aspects of the work, including access to computing resources and large collections of time-series data. There is much flexibility to adjust the specific project to the interests of the student, who should have a strong interest in data science (with a quantitative background in e.g., physics, mathematics, statistics, engineering, or computer science) and working in an interdisciplinary team. Top-up funding is available for the highest quality of applicants, with additional funding available to support travel to present research results at national and international conferences (or to visit collaborators, e.g., at Monash University or Imperial College London).


HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

time-series analysis, data science, Machine learning, feature spaces, complex systems, dynamical systems, nonlinear dynamics, Statistics, Applied mathematics

Opportunity ID

The opportunity ID for this research opportunity is: 2386

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