This course aims to introduce methods for time-series analysis, modelling and forecasting with computing application, and interpretation. The major time series models such as time trend, cycle and seasonal modelling, smoothing, stationary and unit roots tests, causality analysis, distributed lag model, Box-Jenkins ARIMA model, dummy dependent model, vector autoregression (VAR) and error correction (ER) model, ARCH and GARCH model, etc. The course shows how to apply these techniques to real-life data and to develop forecasting models for economic, business and finance applications. Computing is an integral part of this course.