Applied Time Series Analysis

Instructor: Calvin L. Williams, Ph.D.

Text: Introduction to Time Series and Forecasting

Authors: Peter J. Brockwell and Richard A. Davis

Course Objective

Time series analysis refers to problems in which observations are collected at regular (sometimes irregular) time intervals generally with some correlation amongst successive observations. Applications cover virtually all areas of Statistics but some of the most important include economic and financial time series data (stock market, derivatives, etc.), medical and biostatistical time series data (growth curves, longitudinal data, etc.), engineering and the physical sciences (signal processing, etc.) and environmental or ecological data. In this course, we will cover some of the more important methods for dealing with these types of data.


  • Chapter 1: 1.4, 1.5, 1.6, 1.16
  • Chapter 1: 1.11, 1.12
    Other Sources:
  • Applied Bayesian Forecasting and Time Series Analysis
  • Bermuda Atlantic Time Series Data
  • Real Time Toolbox
  • TSA(for Matlab)(zipped)
  • ARFIT(for Matlab)(zipped)
  • Time Series Data Library
    MthSc809 Class Datasets:
  • My Place

    Author: Calvin L. Williams, Mathematical Sciences-Clemson University, Clemson University
    Last updated: January 7, 1998
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