Time-series data represent a major category of real-world data collected over time from various sensors or measurement equipment. This course introduces foundational methods for analyzing time-series data, in particular, about time-series modeling and prediction. We start from investigating the basic properties of time-series data, then discuss a range of popular models widely used for time-series modeling and prediction such as Autoregressive Integrated Moving Average (ARIMA) models, Neural Network (NN), Physics-Informed Neural Network (PINN), Hidden Markov Model (HMM) and Kalman Filter (KF) etc. Besides supervised learning, we also discuss un-supervised learning such as clustering algorithms and Self-Organizing Map (SOM) for analyzing time-series data. Broadly this course is a fundamental course for the students who intend to master essential theoretical methods and practical skills needed to develop, assess, and deploy intelligent functionalities in smart electronic and computer systems, Internet-of-Things (IoT), cyber-physical systems (CPS), and any forecasting-relevant applications in finance, economics, data analytics, and other sciences. Grading Basis: Pass or Fail