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)
MthSc809 Class Datasets:
My Place
Canadian Lynx
Weather Balloon
Snow Data
Wind Data Station RPT
Wind Data Station VAL
Wind Data Station ROS
Wind Data Station KIL
Wind Data Station SHA
Wind Data Station BIR
Wind Data Station DUB
Wind Data Station CLA
Wind Data Station MUL
Wind Data Station CLO
Wind Data Station BEL
Wind Data Station MAL
Temp-Not Pertinant to 809
Author:
Calvin L. Williams,
Mathematical Sciences-Clemson University,
Clemson University
Last updated:
January 7, 1998
Send Comments to :
calvinw@math.clemson.edu