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.
Assignments:
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
Send Comments to :
calvinw@math.clemson.edu