The course aims to provide students with an understanding of statistical methods for time series data, where the order of observation is crucial for depicting the underlying dynamics of related social economic, industry growth, and scientific phenomena. Students will learn to use various time series models and techniques such as exponential smoothing and ARIMA, to model and make forecasts. Additionally, programming techniques to support these practices will be introduced using platforms like MATLAB, R, or AI software packages. Case studies will be provided to familiarise students with basic AI techniques, and real-world applications, such as crop growth, financial development and poverty, economic growth, and industrial development, will be discussed.