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Stat 443: Forecasting
Midterm  February 28, 2014
6:307:30 pm I
(Family . ame
Name (Print):
(Given name)
Section: 2:303:5pm or 4:005: pm UW Student ID Number:
Aids: Calculator, English to other language dictionary
QUESTION MARK
TOTAL

6
7
STAT 443: Assignment 2
(Winter 2013)
This assignment is due in class on Friday March 15th. Please make sure that you
write your name and ID number on the front page of your assignment. For
the data analysis section, you should hand in the R output as well
Time Series Practical Midterm 1 Solutions
1) The following results are derived from the training set (i.e. December 2000, December 2010). a)
Figure : Scatter and Diagnostic Plots of JTUJOL
Figure 1 displays the scatter plot (the left hand side) and the AC
Practice Questions 2
Stat 443 (Winter 2013)
These are some practice questions to prepare you for the midterm and the nal exam. I
will NOT post solutions online for these questions, but you can come to oce hours or
ask your specic questions on the discussi
Stat 443: Forecasting (Fall 2013)
Assignment #2
Due date: Thursday, November 7th in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=Reza , 002=Surya):
1
STAT 443: Assignment 2
(Fall 2013)
This assignment is due in class on Thu
Practice Questions 3
Stat 443 (Winter 2013)
These are some practice questions to prepare you for the midterm and the nal exam. I
will NOT post solutions online for these questions, but you can come to oce hours or
ask your specic questions on the discussi
Practice Questions 1
Stat 443 (Winter 2013)
These are some practice questions to prepare you for the midterm and the nal exam. I
will NOT post solutions online for these questions, but you can come to oce hours or
ask your specic questions on the discussi
STAT 443: Assignment 2: Solutions
Only mark the parts of the assignments which are indicated in this mark
scheme. The total mark for this assignment is 62.
Please indicate to the students where they are losing marks and put your
initials at the top of eac
Homework 2
STAT 443
Spring 2007
1. (20 points) Let cfw_Zt be a sequence of independent normal random
variables with zero mean (E(Zt)=0) and common finite variance
(Var(Zt)=s^2). For each process below define the mean and autocovariance
function. Which, if
HoltWinters Algorithm
HoltWinters Algorithm
HoltWinters method
This generalises exponential smoothing to the case where
there is a trend and seasonality
Following Chateld and Yar (1988) dene trend as
longterm change in the mean level per unit time
Hav
Midterm Theoretical Part STAT 443 Spring 2011
Last Name First Name
1. Since Xt and Yt are weakly stationary, the rst and second moments do not depend on t. X Y Let E(Xt ) = X , Cov(Xt , Xt+h ) = h , E(Xt ) = Y , Cov(Yt , Yt+h ) = h . Then,
E(Xt + Yt ) = X
TIME SERIES FINAL PROJECT
MARCANDRE ROUSSEAU
1. Introduction The topic of this project is Gegenbauer models for long memory processes. In this report, we will discuss the paper by P.M. Lapsa and fit this model to the Farallon data set. There will also be
Homework 2
STAT 443
Spring 2012
DESCRIPTIVE ABSTRACT:
The datafile contains 11 years of quarterly sales for four kinds of retail
establishments, along with nonagricultural employment and wage and salary
disbursements. The task is to develop a model for p
2. A)
plot of data
VALUE
1.0 19930401 19941201 19960801 19980401 19991201 20010801 20030401 20041201 20060801 20080401 20091201 DATE
1.5
2.0
2.5
3.0
3.5
4.0
Fig 1 Plot of DATA There seems no seasonal pattern. Compare SSE of different
1.
If X t and Y t are uncorrelated (weakly) stationary sequences, i.e., if Xr and Y s are
uncorrelated for every r and s, show that Xt +Y t is (weakly) stationary with autocovariance function equal to the sum of the autocovariance functions of X t and Y t
Midterm STAT 443 Spring 2009 1. If X_t and Y_t are uncorrelated (weakly) stationary sequences, i.e.,
if X_r ans Y_s are uncorrelated for every r and s, show that X_t+Y_t is (weakly) stationary with autocovariance function equal to the sum of the auto
Homework 2 STAT 443 Spring 2009 1. Let Zt be a sequence of independent normal random
variables with zero mean and common finite standard deviation s. Let a, b and c be constants. Which, if any, of the following processes are weakly stationary? Justif
Assignment 3 Solutions
1 a) The following are the plots of the time series, ACF and PACF (of all the given data):
Figure : Scatter Plot for Whole Dataset
Figure : ACF and PACF Plots of Whole Dataset
One can see from the time series above that the data are
STAT 443: Assignment 1
SOLUTIONS
Only mark the parts of the assignments which are indicated in this mark
scheme. The total mark for this assignment is 63.
Please indicate to the students where they are losing marks and put your
initials at the top of each
Model selection
Making Predictions
Some Tests Based on Residuals
Forecasting and Regression
Part II: Model selection and diagnostics
1 / 28
Model selection
Making Predictions
Some Tests Based on Residuals
Some Model Selection Methods
R 2 and adjusted R 2
Examples
Examples for Sample ACF (h)
All the R codes for these slides are available on LEARN
Sample ACF
Examples
iid noise
The data is 100 realizations of iid noise N(0, 1).
X
3
1 0
1
2
iid noise
0
20
40
60
80
100
Index
0.6
0.2
0.2
ACF
1.0
sample ACF for
Identication of p,d,q,P,D,Q,s in
SARIMA(p, d, q) (P, D, Q)s
1/7
wine data
12 years of monthly wine consumption in a country. Let us
show the data by Xt
Check for stationarity (trend and seasonality)
Remove the nonseasonal trend by Yt = (1 B)d Xt
Remove s
DESCRIPTIVE ABSTRACT: The dataset ConsIndex.txt contains monthly
price indices on food (FoodInd), beverage (BevInd) and industrial materials
(IndustInd) from January 1991 to March 2012.
SOURCE: International Monetary Fund (IMF) prepared by the Commodities
K,
"SM: 32, moulij
STAT 443: Assignment #3
(Spring 2014)
This assignment is due in class on Wednesday July 30. For the data analysis sec
tion, you should hand in the R code and output, as well as your interpretations of the
outputs. Please make sure that
STAT 443 Winter 2015 Assignment 4
due Thursday March 26 at the beginning of class
You may work in pairs if you choose; both names and ID numbers should appear on it, and both will
receive the same mark. (No extra credit will be given for working alone.)
F
STAT 443 Winter 2015 Assignment 4 SOLUTIONS
1. You have the following three data points from a time series: X1 = 0.5, X2 = 0.3, X3 = 0.3. For
each of the following proposed ARM A(p, q) models for the process, predict (by hand) the next three
c4 , X
c5 , a