Muhammad Sakib Khan Inan
I am a 3rd year PhD candidate at Deakin University, specialising Artificial Intelligence (AI) for time series data analysis from heterogeneous IoT sensors. My research focuses on developing novel AI methods to classify time series measurements and recover the lost identity (metadata) of IoT sensors. I am passionate about building robust, intelligent systems that make sense of complex sensor data in real-world environments. I have been working with Python for over six years and regularly apply it to machine learning, data processing, and scientific research tasks.
He/Him
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.