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Turning time into insight for sharper decisions and smarter forecasting

The Temporal Analytics Lab focuses on understanding patterns, making predictions and detecting unusual behaviour in data over time – driving outcomes such as proactive healthcare, reliable disaster predictions and thriving societies.

Our Lab is home to a range of innovative tools and projects – including the only current AI-focused Australian Laureate initiative led by world-renowned expert Professor Geoff Webb, developing AI systems that can understand a continuously changing world.

Key research areas

  • Time series anomaly detection

    Designing  techniques for identifying unusual patterns or outliers in time series data, supporting early detection of faults, risks and rare events.

  • Time series classification

    Investigating technologies for automatically assigning labels to time-based data, enabling tasks such as identifying activities, system states or patterns in each observation over time.

  • Time series forecasting

    Inventing  models for predicting future values from historical data, enhancing our ability to accurately anticipate trends such as demand, weather or financial changes.

  • Time series segmentation

    Exploring new methods for breaking continuous time series data into meaningful segments, making it easier to detect changes and interpret different phases over time.

  • Learning in the context of non-stationary distributions

    Uncovering how to build adaptive models that remain accurate when data patterns change over time, such as in evolving environments or shifting user behavior.

  • Applications of time series analytics

    Developing and applying time series methods across domains such as healthcare, science, energy, and the environment to address real-world challenges and enable better decision-making.

Learn more about the lab

For any additional questions or inquiries, feel free to reach out
at fit-temporal-analytics-lab@monash.edu.