Rubaiath E Ulfath
I am a second-year PhD candidate at RMIT University, specialising in Artificial Intelligence for optimising energy efficiency and operational costs in centralised chilled water plants. Passionate about using technology to drive positive change, I am dedicated to solving real-world challenges through Machine Learning, Deep Learning, and advanced time series forecasting. My research, part of the RACE for 2030 Research Programme, focuses on building predictive models that enhance cooling load forecasting and reduce Total Cost of Ownership (TCO) for sustainable energy solutions.
With recent experience in AI-driven cybersecurity, Natural Language Processing, and predictive modelling, I am committed to expanding my technical expertise and translating research into actionable insights for industry. I thrive on collaboration with industry experts and the broader tech community, sharing ideas, learning, and exploring AI’s transformative potential in energy, sustainability, and beyond.
Session
Time series data is everywhere. Across industries such as environmental monitoring, financial market analysis, power and energy systems, and scientific discovery, organisations rely on analysing large volumes of complex time series data to make smart and informed decisions that help keep the world running smoothly. Two of the most critical tasks in time series analysis are Time Series Classification and Time Series Forecasting. Python’s data science ecosystem for time series analysis has grown significantly in recent years. In this talk, we will introduce the modern landscape of time series tools available in Python. We will demonstrate the usability, algorithmic diversity, and interface design of libraries such as Sktime, Aeon, and Nixtla (NeuralForecast, MLForecast). These libraries will serve as examples to show how easy they are to use, what kinds of algorithms they provide, and how their application programming interfaces are structured to support efficient and intuitive development. Whether your goal is to classify environmental patterns or forecast future trends, these tools can simplify and accelerate your time series analysis workflow.