Time Series Analysis


Course description

Definition and concepts; Objectives; Time series data, Components of time series analysis; Additive and multiplicative models; Tests of randomness; Constant mean model,  estimation methods; Linear trend estimation using moving average, Least squares, Exponential smoothing methods; Seasonal variation, estimation using simple average, moving average, dummy variable, exponential smoothing; Cyclical variation; Introduction to Box-Jenkins models: Stationary, Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), ARIMA models; Forecasting Methods.

Objectives

The objectives of the course are:

  • to introduce students to the theory and methods of time series analysis;
  • to equip students with skills of applying various time series models;
  • to enable students to use standard software for related computations;