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Stat 443: Forecasting
Midterm - February 28, 2014
6:307:30 pm I
(Family . ame
Name (Print):
(Given name)
Section: 2:30-3:5pm or 4:005: pm UW Student ID Number:
Aids: Calculator, English t
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 di
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 an
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Stat 443: Forecasting
Test #3 November 6, 2014
8:30 9:50 am
Name (Print): ,
(Family name) (Given name)
UW Student ID Number:
Aidsz. Calculator, English to other language dictionary
M
QUES
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
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: Assignme
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
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 a
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
STAT 443:
Forecasting
Paul Marriott
Introduction
Nonstationary
time series
Forecasting
ARIMA
models
STAT 443: Box-Jenkins approach
Using R
SARIMA
modelling
Using R
The Box
Jenkins
Approach
Paul Marrio
STAT 443 Fall 2016 Assignment 2 Solutions
1. Consider a special stationary AR(2) process with 1 = 0. That is, Xt = Xt2 + Zt , where Zt
W N (0, 2 ) and Zt is uncorrelated with Xs for t > s
(a) Find th
TIME SERIES FINAL PROJECT
MARC-ANDRE 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
Holt-Winters Algorithm
Holt-Winters Algorithm
Holt-Winters method
This generalises exponential smoothing to the case where
there is a trend and seasonality
Following Chateld and Yar (1988) dene trend
Homework 2
STAT 443
Spring 2012
DESCRIPTIVE ABSTRACT:
The datafile contains 11 years of quarterly sales for four kinds of retail
establishments, along with non-agricultural employment and wage and sal
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
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
O
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
STAT 443:
Forecasting
Paul Marriott
Introduction
Examples
Foundations
and
Denitions
STAT 443: Forecasting
Winter 2012
Paul Marriott
[email protected]
M3 3004
STAT 443:
Forecasting
Overview
Paul Ma
2. A)
plot of data
VALUE
1.0 1993-04-01 1994-12-01 1996-08-01 1998-04-01 1999-12-01 2001-08-01 2003-04-01 2004-12-01 2006-08-01 2008-04-01 2009-12-01 DATE
1.5
2.0
2.5
3.0
3.5
4.0
Fig 1 Plot of DATA Th
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
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 au
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
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 ar
Stat 4433: Forecasting
Test #1 - September 25, 2014
5:30 - 6:20 pm
Name (Print):
(Family name) (Given name)
UW Student ID Number:
Aids: Calculator, English to other language dictionary
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i.
STAT 443:
Forecasting
Paul Marriott
Introduction
Computing in R
Model
complexity
Bias-variance
decomposition
Subset selection
STAT 443: Regression methods
and model building principles
Paul Marriott
p
STAT 443:
Forecasting
Paul Marriott
Preamble
Introduction
Examples
Observed data
examples
STAT 443: Forecasting
Paul Marriott
[email protected]
M3 4204
STAT 443:
Forecasting
Overview
Paul Marriott
STAT 443:
Forecasting
Paul Marriott
Review
The Trivial
Case
Mathematical
models
STAT 443: Forecasting
Paul Marriott
[email protected]
M3 4204
STAT 443:
Forecasting
Last Lecture
Paul Marriott
Revie