A5000087814- Regional Socio-Economic Development Trends
- COVID-19, Geopolitics, Technology, Migration
- Impact of AI and Big Data on Business and Society
- Time Series Analysis and Forecasting
- Energy Load and Power Forecasting
- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Wikis in Education and Collaboration
- Web Data Mining and Analysis
- Artificial Intelligence in Healthcare
- Big Data Technologies and Applications
- Solar Radiation and Photovoltaics
- Smart Grid Energy Management
- Human Mobility and Location-Based Analysis
- Text and Document Classification Technologies
- Machine Learning in Healthcare
- Solar and Space Plasma Dynamics
- Computational Physics and Python Applications
- Data Management and Algorithms
- Traffic Prediction and Management Techniques
- Data Stream Mining Techniques
- Transportation and Mobility Innovations
- COVID-19 epidemiological studies
- Manufacturing Process and Optimization
- Precipitation Measurement and Analysis
The University of Sydney
2020-2025
Universidade da Coruña
2018-2020
Monash University
2018-2020
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here, we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022.
Many businesses and industries nowadays rely on large quantities of time series data making forecasting an important research area. Global models that are trained across sets have shown a huge potential in providing accurate forecasts compared with the traditional univariate work isolated series. However, there currently no comprehensive archives for contain datasets from similar sources available community to evaluate performance new global algorithms over wide variety datasets. In this...
Abstract Background Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022. Methods We used open-source tools develop a public European Forecast Hub. invited groups...
Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, transition towards carbon-free energy generation battery/load/production scheduling sustainable systems. Typically, these scenarios we want solve an problem depends on unknown future values, which therefore need be forecast. As problems their own right, relatively few research has been done this area. This...
Abstract A new test statistic using interpoint distances is proposed to address the two‐sample problem for multivariate populations. The compares univariate distributions of within and between samples pairwise a Cramér‐von Mises‐type statistic. critical values are approximated by means permutation procedure regularity conditions required ensure consistency established. Unlike other procedures, our approach whole instead just few moments, thus obtaining higher capability detect differences in...
Abstract Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation shared genes during response different types of disturbances (termed as perturbome), leading the idea central control at molecular level. We implemented a robust machine learning approach identify describe associated with multiple perturbations or perturbome in Pseudomonas...
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, TSF are handcrafted, which requires domain knowledge significant data-engineering work. this research, we first introduce a notion of static dynamic features, then enables us develop our autonomous Retrieving Autoregressive Network for Static (FRANS) that does not require...