
Department of Statistics
Stanford University
Summer 2001


Stat 207: Introduction to Time Series Analysis
Week 8:
 The final is on Friday August 17, 12:15  3:15.
 Homework #6 is due of Friday.
 The Project is due on Friday.
 This week we will finish some remaining topics related to Frequency Domain
Analysis, we will discuss signal detection, and have a brief introduction to
State Space Models.
Week 7:
 This week we will cover Frequency Domain analysis.
 Homework #6 has been assigned.
 Two handouts related to Chapter 3 have been posted.
Week 6:
 The solution to the inclass midterm is now available.
 This week we will finish Estimation, we will discuss Regression with
Autocorrelated Errors and Transfer Function Modeling, and begin the
Frequency Domain Analysis of Time Series Data.
 Homework #5 has been assigned.
 You should begin looking for a dataset of interest to you to analysis for
the class project. You might consider looking at these two
websites.
 For problem 2.36 on the homework, if you are using R you will need to
download and install the fracdiff library. To install a library you
need to download the file containing the library from the Rproject
contributed packages website and then run the install program selecting
where the library file is and then where you want to install it. The
directory you should install to in the \R\library\ directory. To call
the library, library(fracdiff). I have posted an example on the Handouts
page.
 For problems 2.39 which is about regression with autocorrelated errors, I
suggest that you use ASTSA's Multiple Regression option under Time Domain
and the ARIMA residual option under Transform and then Transform. This
problem can also be done using Splus, using the arima.filt( ) function, or
using R, using the filter( ) funtion.
 For problems 2.41 and 2.42 which are related to transfer function
modeling, I suggest that you use ASTSA and the options mentioned
above. The data file is climhyd.asd.
Week 5:
 The Inclass midterm is on Tuesday. I plan to have it graded so it
can be returned on Thursday.
 The Takehome Midterm has three
datasets: racife.dat securities.dat
airpass.dat
 There are also two handouts related to performing the Classical
Decomposition of a Racife, Brazil Air Temperature data. racife.r
racife.ssc
 The fourth homework assignment and the Takehome Midterm are both due on
Monday next week.
Week 4:
 This week we will cover model ARIMA development and selection,
forecasting, and estimation.
 See Homework for the simulated
datasets for Problems 2.6 and 2.8.
 See Handouts for more Splus examples.
 See Homework for the solutions to
Homework 1 and 2.
 The midterm will be next Tuesday in class and there will be a takehome
part of the midterm that will be due the following Monday.
Week 3:
 This week we will introduce the general ARMA model.
 See Handouts for an Splus code that
simulated ARIMA data and plots the ACF's and PACF's and fits ARIMA models to
the simulated data.
Week 2:
 Introduction to time domain modeling.
 The datasets
from the book are available from both of the author's websites in dos format
and in unix format. See the Books above
link also.
 See Handouts for more Splus examples.
Week 1:
 I plan to be on campus Monday June 25 in the afternoon if anyone has any
questions.
 Class begins on June 26.
Week 0:
 The book I have chosen for the course is Shumway and Stoffer: Time Series
Analysis and Its Applications, Springer Verlag, 2000.
