Course Objective

To introduce students to statistical and probability methods used in analyzing and interpreting time dependent data.

Course Learning Outcomes

At the end of this course, the students should be able to

• Fit different kinds of statistical models to time dependent data

• Analyze a time series and decompose it into its components

• Predict or forecast values with some given degree of accuracy or certainty.

Course Description

Introduction: Definition of time series and typical examples. Fitting time dependent data: Polynomial, Exponential and Logistic. Harmonic Analysis, Weiner’s approach to time series. Linear time-related invariant filters: simple moving average filters and difference filters. Estimation of trends and seasonal effects.Stationary time series. Autoregressive Moving Average Processes. Forecasting.