ISYE 6402 TIME SERIES ANALYSIS MIDTERM 1 PRACTICE EXAM
WITH COMPLETE SOLUTIONS | MT. EVEREST TEMPERATURE
DATA | QUESTIONS AND ANSWERS | 2026 UPDATE | 100%
CORRECT - GT.
179 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
ISYE 6402 TIME SERIES ANALYSIS MIDTERM 1 PRACTICE EXAM WITH COMPLETE SOLUTIONS | MT.
EVEREST TEMPERATURE DATA | QUESTIONS AND ANSWERS | 2026 UPDATE | 100% CORRECT - GT.. It
contains 179 carefully selected questions that reflect the most current exam content and testing strategies. Each
question is accompanied by a correct answer and a detailed rationale that explains the underlying
pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 179 Questions
Foundations - Application - ISYE 6402 TIME Series Analysis 1 WITH Complete Solutions MT Everest
Temperature DATA AND 2026 Update 100 Correct - GT TIME Series Analysis Graduate
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO TIME Series 1-30 Model, Series, Temperature, Arima, Everest
Analysis
Stationarity AND 31-60 Model, Series, Temperature, Arima, Everest
Autocorrelation
Trend AND Seasonality 61-90 Series, Model, Temperature, Everest, Arima
Decomposition
AR MA ARMA AND Arima 91-120 Model, Series, Temperature, Everest, Appropriate
Models
Model Identification AND 121-150 Temperature, Model, Series, Everest, Arima
Estimation
Model Diagnostics AND 151-179 Model, Temperature, Series, Arima, Everest
Residual Analysis
TOTAL 179 All questions include answers and detailed rationales
,Section A - Introduction TO TIME Series Analysis
Q1.
Given the augmented Dickey-Fuller (ADF) test on Mt. Everest annual mean temperatures
yields a test statistic of -2.5 with a p-value of 0.12, and the KPSS test statistic is 0.4
(critical value 0.46 at 5%). Which conclusion is most defensible?
A. The series is stationary; both tests agree. B. The series is non-stationary; ADF fails to
reject null but KPSS fails to reject null,
indicating conflicting evidence.
C. The series is non-stationary; ADF fails to D. The series is stationary; KPSS fails to
reject null of unit root, KPSS fails to reject reject null of stationarity, and ADF rejection
null of stationarity, leading to inconclusive is not required.
evidence.
Correct: C - The series is non-stationary; ADF fails to reject null of unit root, KPSS fails to
reject null of stationarity, leading to inconclusive evidence.
Rationale:ADF test null hypothesis is non-stationarity; failing to reject suggests
non-stationary. KPSS null is stationarity; failing to reject suggests stationary. Conflict means
inconclusive. Options A, B, D misinterpret hypotheses.
Q2.
For a daily temperature series with strong annual periodicity, you fit an ARIMA(2,1,2)
model. The residuals show significant autocorrelation at lag 365. Which modification is
most appropriate?
A. Increase AR order to 365. B. Add a seasonal component, e.g.,
SARIMA(2,1,2)(1,1,1)[365].
C. Apply a log transformation to stabilize D. Use a non-parametric trend removal
variance. method.
Correct: B - Add a seasonal component, e.g., SARIMA(2,1,2)(1,1,1)[365].
Rationale:Residual autocorrelation at seasonal lag indicates unmodeled seasonality. Adding
seasonal ARIMA terms directly addresses periodic structure. Increasing AR order to 365 is
inefficient and not parsimonious; log transform doesn't address autocorrelation; trend removal
is irrelevant.
Q3.
In spectral analysis of monthly temperature anomalies, a peak at frequency 0.083
cycles/month indicates a period of approximately:
A. 1 year B. 1 month
Page 3
, Section A - Introduction TO TIME Series Analysis
C. 12 years D. 8.3 months
Correct: A - 1 year
Rationale:Period = 1/frequency = 1/0.083 "H 12 months, i.e., 1 year. Other options
miscompute or misplace decimal.
Q4.
You fit an AR(1) model to the detrended annual temperature series: X_t = 0.7 X_{t-1} + _t,
with _t white noise variance 1. What is the variance of X_t?
A. 1.96 B. 0.49
C. 1.0 D. 1.43
Correct: A - 1.96
Rationale:Variance of stationary AR(1) = ò/(1-Ʋ) = 1/(1-0.49) = 1/0.51 "H 1.96. Other options
are misapplications of formula or ignore denominator.
Q5.
When comparing two competing ARIMA models using AIC and BIC, which statement is
correct?
A. AIC penalizes model complexity more B. BIC is consistent but may choose overly
heavily than BIC. complex models in large samples.
C. AIC is asymptotically efficient but not D. Both criteria always select the same
consistent; BIC is consistent but may be model.
inefficient in small samples.
Correct: C - AIC is asymptotically efficient but not consistent; BIC is consistent but may
be inefficient in small samples.
Rationale:AIC minimizes prediction error and is efficient but not consistent; BIC is consistent
but may underfit in small samples. A is false; BIC penalizes more heavily. B is false; BIC is
consistent. D is false.
Q6.
For a non-stationary temperature series, you apply first differencing and obtain a series
with ACF that cuts off after lag 1 and PACF that tails off. Which model is most appropriate
for the differenced series?
A. AR(1) B. MA(1)
C. ARMA(1,1) D. White noise
Page 4
WITH COMPLETE SOLUTIONS | MT. EVEREST TEMPERATURE
DATA | QUESTIONS AND ANSWERS | 2026 UPDATE | 100%
CORRECT - GT.
179 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
ISYE 6402 TIME SERIES ANALYSIS MIDTERM 1 PRACTICE EXAM WITH COMPLETE SOLUTIONS | MT.
EVEREST TEMPERATURE DATA | QUESTIONS AND ANSWERS | 2026 UPDATE | 100% CORRECT - GT.. It
contains 179 carefully selected questions that reflect the most current exam content and testing strategies. Each
question is accompanied by a correct answer and a detailed rationale that explains the underlying
pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 179 Questions
Foundations - Application - ISYE 6402 TIME Series Analysis 1 WITH Complete Solutions MT Everest
Temperature DATA AND 2026 Update 100 Correct - GT TIME Series Analysis Graduate
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO TIME Series 1-30 Model, Series, Temperature, Arima, Everest
Analysis
Stationarity AND 31-60 Model, Series, Temperature, Arima, Everest
Autocorrelation
Trend AND Seasonality 61-90 Series, Model, Temperature, Everest, Arima
Decomposition
AR MA ARMA AND Arima 91-120 Model, Series, Temperature, Everest, Appropriate
Models
Model Identification AND 121-150 Temperature, Model, Series, Everest, Arima
Estimation
Model Diagnostics AND 151-179 Model, Temperature, Series, Arima, Everest
Residual Analysis
TOTAL 179 All questions include answers and detailed rationales
,Section A - Introduction TO TIME Series Analysis
Q1.
Given the augmented Dickey-Fuller (ADF) test on Mt. Everest annual mean temperatures
yields a test statistic of -2.5 with a p-value of 0.12, and the KPSS test statistic is 0.4
(critical value 0.46 at 5%). Which conclusion is most defensible?
A. The series is stationary; both tests agree. B. The series is non-stationary; ADF fails to
reject null but KPSS fails to reject null,
indicating conflicting evidence.
C. The series is non-stationary; ADF fails to D. The series is stationary; KPSS fails to
reject null of unit root, KPSS fails to reject reject null of stationarity, and ADF rejection
null of stationarity, leading to inconclusive is not required.
evidence.
Correct: C - The series is non-stationary; ADF fails to reject null of unit root, KPSS fails to
reject null of stationarity, leading to inconclusive evidence.
Rationale:ADF test null hypothesis is non-stationarity; failing to reject suggests
non-stationary. KPSS null is stationarity; failing to reject suggests stationary. Conflict means
inconclusive. Options A, B, D misinterpret hypotheses.
Q2.
For a daily temperature series with strong annual periodicity, you fit an ARIMA(2,1,2)
model. The residuals show significant autocorrelation at lag 365. Which modification is
most appropriate?
A. Increase AR order to 365. B. Add a seasonal component, e.g.,
SARIMA(2,1,2)(1,1,1)[365].
C. Apply a log transformation to stabilize D. Use a non-parametric trend removal
variance. method.
Correct: B - Add a seasonal component, e.g., SARIMA(2,1,2)(1,1,1)[365].
Rationale:Residual autocorrelation at seasonal lag indicates unmodeled seasonality. Adding
seasonal ARIMA terms directly addresses periodic structure. Increasing AR order to 365 is
inefficient and not parsimonious; log transform doesn't address autocorrelation; trend removal
is irrelevant.
Q3.
In spectral analysis of monthly temperature anomalies, a peak at frequency 0.083
cycles/month indicates a period of approximately:
A. 1 year B. 1 month
Page 3
, Section A - Introduction TO TIME Series Analysis
C. 12 years D. 8.3 months
Correct: A - 1 year
Rationale:Period = 1/frequency = 1/0.083 "H 12 months, i.e., 1 year. Other options
miscompute or misplace decimal.
Q4.
You fit an AR(1) model to the detrended annual temperature series: X_t = 0.7 X_{t-1} + _t,
with _t white noise variance 1. What is the variance of X_t?
A. 1.96 B. 0.49
C. 1.0 D. 1.43
Correct: A - 1.96
Rationale:Variance of stationary AR(1) = ò/(1-Ʋ) = 1/(1-0.49) = 1/0.51 "H 1.96. Other options
are misapplications of formula or ignore denominator.
Q5.
When comparing two competing ARIMA models using AIC and BIC, which statement is
correct?
A. AIC penalizes model complexity more B. BIC is consistent but may choose overly
heavily than BIC. complex models in large samples.
C. AIC is asymptotically efficient but not D. Both criteria always select the same
consistent; BIC is consistent but may be model.
inefficient in small samples.
Correct: C - AIC is asymptotically efficient but not consistent; BIC is consistent but may
be inefficient in small samples.
Rationale:AIC minimizes prediction error and is efficient but not consistent; BIC is consistent
but may underfit in small samples. A is false; BIC penalizes more heavily. B is false; BIC is
consistent. D is false.
Q6.
For a non-stationary temperature series, you apply first differencing and obtain a series
with ACF that cuts off after lag 1 and PACF that tails off. Which model is most appropriate
for the differenced series?
A. AR(1) B. MA(1)
C. ARMA(1,1) D. White noise
Page 4
