Machine Learning approaches to timeseries predictions – Victorian pollen season 2019

Usha Nattala1

1The University of Melbourne

 

Abstract:

Australia has one of the highest rates of allergies in the world with 1 in 9 Australians affected by asthma and the 1 in 7 Australians with hay fever. Through a combination of innovative and cutting-edge interdisciplinary research, digital technologies and citizen science, the Melbourne Pollen project engages with thousands of Victorians each day during the peak spring allergy period (October-December), helping to keep the state safe from deadly thunderstorm asthma and improve the health of Victorians. Previously, statistical models were used to generate 7-day forecasts to predict pollen levels in the air. In 2019, we trialled machine learning techniques and packages such as traditional ML methods, Prophet, ARIMA and LSTMs with varying levels of accuracy. Our talk focuses on the shortcomings and strengths of each approach.


Biography:

Usha is a research data specialist and software developer with over a decade of experience across multiple technology stacks. Specializing in application development, data science, machine learning and math-intensive programming, she quite enjoys the process of bringing to life the ideas of the best and brightest minds of our generation.

Usha works at the Melbourne Data Analytics Platform (MDAP), University of Melbourne. Conceptualized under the Petascale Campus Initiative (PCI), MDAP is the university’s premium data and computational science team helping researchers across the community in the fields of high performance computing, big data, machine learning, natural language processing and text mining, analysis and visualisation, virtual and augmented reality, image and video processing, scientific simulation and modelling, and web application development.

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