A5045218396- Forecasting Techniques and Applications
- Energy Load and Power Forecasting
- Global Health Care Issues
- Statistical Methods and Inference
- Insurance, Mortality, Demography, Risk Management
- Time Series Analysis and Forecasting
- Financial Risk and Volatility Modeling
- Statistical and numerical algorithms
- demographic modeling and climate adaptation
- Data Analysis with R
- Medical Coding and Health Information
- Machine Learning in Healthcare
- Statistical Methods and Applications
- Machine Learning in Bioinformatics
- Bayesian Methods and Mixture Models
- Labor market dynamics and wage inequality
- Gaussian Processes and Bayesian Inference
- Algorithms and Data Compression
- Hemodynamic Monitoring and Therapy
- Advanced Statistical Process Monitoring
- Insurance and Financial Risk Management
- Health and Conflict Studies
- AI in cancer detection
- Energy, Environment, Economic Growth
- Health, Environment, Cognitive Aging
Monash University
2016-2020
Parks Victoria
2018
Australian Regenerative Medicine Institute
2013-2014
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles,...
We propose a new method for decomposing seasonal data: seasonal-trend decomposition using regression (STR). Unlike other methods, STR allows multiple and cyclic components, covariates, patterns that may have noninteger periods, seasonality with complex topology. It can be used time series any regular index, including hourly, daily, weekly, monthly, or quarterly data. is competitive existing methods when they exist tackles many more problems than allow. based on regularized optimization so...
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations additive multiplicative exponential smoothing to model grow faster than linear but slower exponential. Their development is motivated by fast-growing, volatile series. In particular, our have global trend smoothly change from combined with local trend. Seasonality, when used, in models, the error always heteroscedastic through parameter sigma. We leverage state-of-the-art...
Mortality rates typically vary smoothly over age and time. Exceptions occur owing to events such as wars epidemics, which create ridges in the age‐period surface of mortality a particular year or for cohorts born year. We propose new practical method modelling rates. Our approach uses L 1 regularization with bivariate smoothing allows age‐varying period cohort otherwise smooth surface. Cross‐validation on data from many countries simulations demonstrates that our is superior existing...
The translation of medical diagnosis to clinical coding has wide range applications in billing, aetiology analysis, and auditing. Currently, is a manual effort while the automation such task not straight forward. Among challenges are messy noisy records, case complexities, along with huge ICD10 code space. Previous work mainly relied on discharge notes for prediction was applied very limited data scale. We propose an ensemble model incorporating multiple sources accurate predictions. further...
Many countries have implemented social programs providing long-term financial or in-kind entitlements. These often focus on specific age-groups and consequently their expenditure streams are subject to demographic change. Given the strains already existing public budgets, forecasts an increasingly important instrument monitor budgetary consequences of programs. The expected development labour force is a key input these forecasts. We Produce age-specific market participation rates, combining...
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations additive multiplicative exponential smoothing models, to model grow faster than linear but slower exponential. Their development is motivated by fast-growing, volatile series. In particular, our have global trend smoothly change from multiplicative, combined with local trend. Seasonality when used in the error always heteroscedastic through parameter sigma. We leverage...
Clinical coding is an administrative process that involves the translation of diagnostic data from episodes care into a standard code format such as ICD10. It has many critical applications billing and aetiology research. The automation clinical very challenging due to sparsity, low interoperability digital health systems, complexity real-life diagnosis coupled with huge size ICD10 space. Related work suffer applicability reliance on sources, inefficient modelling less generalizable...
We propose a new method for decomposing seasonal data: STR (a Seasonal-Trend decomposition using Regression). Unlike other methods, allows multiple and cyclic components, covariates, patterns that may have non-integer periods, seasonality with complex topology. It can be used time series any regular index including hourly, daily, weekly, monthly or quarterly data. is competitive existing methods when they exist, but tackles many more problem than allow. based on regularized optimization, so...