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NOC Applied Time Series Analysis
broucher Course Brochure
NOC Applied Time Series Analysis
by Dr Arun K Tangirala

Intervention Analysis (basically before/after analysis of a time series to assess effect of a new policy, treatment, etc.) Longitudinal Analysis and Repeated Measures Models for comparing treatments when the response is a time series. Vector Autoregressive Models for Multivariate Time Series....

0.0  (0 Ratings) 948 Learner
validy img Validity Unlimited
level All Level
level English
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Program Fee
FREE
About This Course

Intervention Analysis (basically before/after analysis of a time series to assess effect of a new policy, treatment, etc.) Longitudinal Analysis and Repeated Measures Models for comparing treatments when the response is a time series. Vector Autoregressive Models for Multivariate Time Series....


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Course Curriculum
11 Sections | 102 Lecture | 40h 29min total length
Expand all sections
NOC Applied Time Series Analysis

MODULE 1

| 03h 51min total length
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    Lecture01_Part1 - Motivation and Overview 1

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    Lecture01_Part2 - Motivation and Overview 2

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    Lecture02_Part1 - Motivation and Overview 3

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    Lecture02_Part2 - Motivation and Overview 4

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    Lecture03_Part1 - Motivation and Overview 5

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    Lecture03_Part2 - Motivation and Overview 6

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    Lecture04_Part1 - Probability and Statistics Review 1A

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    Lecture04_Part2 - Probability and Statistics Review 1B

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    Lecture05_Part1 - Probability and Statistics Review 1C

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    Lecture05_Part2 - Probability and Statistics Review 1D

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NOC Applied Time Series Analysis

MODULE 2

| 04h 01min total length
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    Lecture06_Part1 - Probability and Statistics Review 2A

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    Lecture06_Part2 - Probability and Statistics Review 2B

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    Lecture06_Part3 - Probability and Statistics Review 2C

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    Lecture07_Part1 - Probability and Statistics Review 2D

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    Lecture07_Part2 - Probability and Statistics Review 2E

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    Lecture07_Part3 - Probability and Statistics Review 2F

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    Lecture08_Part1 - Probability and Statistics Review 2G (with R Demonstration)

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    Lecture08_Part2 - Probability and Statistics Review 2H (with R Demonstration)

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    Lecture9_Part1 - Probability and Statistics Review 2 I

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    Lecture9_Part2 - Probability and Statistics Review 2J

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    Lecture9_Part3 - Introduction to Random Processes 1

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NOC Applied Time Series Analysis

MODULE 3

| 04h 18min total length
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    Lecture10_Part1 - Introduction to Random Processes 2

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    Lecture10_Part2 - Introduction to Random Processes 3

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    Lecture11_Part1 - Introduction to Random Processes 4

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    Lecture11_Part2 - Introduction to Random Processes 5

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    Lecture11_Part3 - Autocovariance & Autocorrelation Functions 1

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    Lecture12_Part1 - Autocovariance & Autocorrelation Functions 2

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    Lecture12_Part2 - Autocovariance & Autocorrelation Functions 3

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    Lecture13_Part1 - Autocovariance & Autocorrelation Functions 4

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    Lecture13_Part2 - Autocovariance & Autocorrelation Functions 5

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    Lecture13_Part3 - Autocovariance & Autocorrelation Functions 6

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    Lecture14_Part1 - Autocovariance & Autocorrelation Functions 7

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NOC Applied Time Series Analysis

MODULE 4

| 04h 07min total length
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    Lecture14_Part2 - Autocovariance & Autocorrelation Functions 8

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    Lecture15_Part1 - Autocovariance & Autocorrelation Functions 9

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    Lecture15_Part2 - Partial Autocorrelation Functions

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    Lecture16_Part1 - Autocorrelation and Partial-autocorrelation Functions

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    Lecture16_Part2 - Models for Linear Stationary Processes 1

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    Lecture17_Part1 - Models for Linear Stationary Processes 2

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    Lecture17_Part2 - Models for Linear Stationary Processes 3

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    Lecture18_Part1 - Models for Linear Stationary Processes 4

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    Lecture18_Part2 - Models for Linear Stationary Processes 5

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    Lecture18_Part3 - Models for Linear Stationary Processes 6

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    Lecture19_Part1 - Models for Linear Stationary Processes 7

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NOC Applied Time Series Analysis

MODULE 5

| 04h 41min total length
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    Lecture19_Part2 - Models for Linear Stationary Processes 8

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    Lecture19_Part3 - Models for Linear Stationary Processes 9

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    Lecture20_Part1 - Models for Linear Stationary Processes 10

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    Lecture20_Part2 - Models for Linear Stationary Processes 11

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    Lecture21_Part1 - Models for Linear Stationary Processes 12

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    Lecture21_Part2 - Models for Linear Stationary Processes 13

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    Lecture22_Part1 - Models for Linear Stationary Processes 14

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    Lecture22_Part2 - Models for Linear Stationary Processes 15

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    Lecture22_Part3 - Models for Linear Stationary Processes 16

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    Lecture23_Part1 - Models for Linear Non-stationary Processes 1

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    Lecture23_Part2 - Models for Linear Non-stationary Processes 2

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NOC Applied Time Series Analysis

MODULE 6

| 04h 02min total length
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    Lecture24_Part1 - Models for Linear Non-stationary Processes 3

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    Lecture24_Part2 - Models for Linear Non-stationary Processes 4

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    Lecture25_Part1 - Models for Linear Non-stationary Processes 5

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    Lecture25_Part2 - Models for Linear Non-stationary Processes 6

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    Lecture26_Part1 - Fourier Transforms for Deterministic Signals 1

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    Lecture26_Part2 - Fourier Transforms for Deterministic Signals 2

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    Lecture27_Part1 - Fourier Transforms for Deterministic Signals 3

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    Lecture27_Part2 - Fourier Transforms for Deterministic Signals 4

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    Lecture28_Part1 - Fourier Transforms for Deterministic Signals 5

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    Lecture28_Part2 - Fourier Transforms for Deterministic Signals 6

    22m <?php echo $alt; ?>
NOC Applied Time Series Analysis

MODULE 7

| 03h 43min total length
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    Lecture29_Part1 - Fourier Transforms for Deterministic Signals 7

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    Lecture29_Part2 - Fourier Transforms for Deterministic Signals 8

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    Lecture30_Part1 - Fourier Transforms for Deterministic Signals 9

    28m <?php echo $alt; ?>
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    Lecture30_Part2 - DFT and Periodogram 1

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    Lecture31_Part1 - DFT and Periodogram 2

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    Lecture31_Part2 - DFT and Periodogram 3 (with R Demonstrations)

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    Lecture32_Part1 - Spectral Representations of Random Processes 1

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    Lecture32_Part2 - Spectral Representations of Random Processes 2

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    Lecture33_Part1 - Spectral Representations of Random Processes 3

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    Lecture33_Part2 - Spectral Representations of Random Processes 4

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NOC Applied Time Series Analysis

MODULE 8

| 04h 15min total length
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    Lecture33_Part3 - Spectral Representations of Random Processes 5

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    Lecture34_Part1 - Spectral Representations of Random Processes 6

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    Lecture34_Part2 - Spectral Representations of Random Processes 7

    33m <?php echo $alt; ?>
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    Lecture35_Part1 - Introduction to Estimation Theory 1

    19m <?php echo $alt; ?>
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    Lecture35_Part2 - Introduction to Estimation Theory 2

    27m <?php echo $alt; ?>
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    Lecture35_Part3 - Introduction to Estimation Theory 3

    31m <?php echo $alt; ?>
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    Lecture 36A - Introduction to Estimation Theory -4

    24m <?php echo $alt; ?>
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    Lecture 36B - Goodness of Estimators 1 -1

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    Lecture 37A - Goodness of Estimators 1 -2

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    Lecture 37B - Goodness of Estimators 1 -3

    25m <?php echo $alt; ?>
NOC Applied Time Series Analysis

MODULE 9

| 03h 56min total length
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    Lecture 37C - Goodness of Estimators 1 -4

    10m <?php echo $alt; ?>
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    Lecture 38A - Goodness of Estimators 2 -1

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    Lecture 38B - Goodness of Estimators 2 -2

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    Lecture 38C - Goodness of Estimators 2 -3

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    Lecture 39A - Goodness of Estimators 2 -4

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    Lecture 39B - Goodness of Estimators 2 -5 (with R demonstrations)

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    Lecture 39C - Goodness of Estimators 2 -6

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    Lecture 40A - Goodness of Estimators 2 -7

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    Lecture 40B - Goodness of Estimators 2 -8

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    Lecture 41A - Estimation Methods 1 -1

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    Lecture 41B - Estimation Methods 1 -2

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    Lecture 42A - Estimation Methods 1 -3

    25m <?php echo $alt; ?>
NOC Applied Time Series Analysis

MODULE 10

| 02h 29min total length
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    Lecture 47A - MLE and Bayesian Estimation -3

    21m <?php echo $alt; ?>
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    Lecture 47B - MLE and Bayesian Estimation -4

    21m <?php echo $alt; ?>
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    Lecture 48A - Estimation of Time Domain Statistics -1

    28m <?php echo $alt; ?>
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    Lecture 48B - Estimation of Time Domain Statistics -2

    28m <?php echo $alt; ?>
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    Lecture 49 - Periodogram as PSD Estimator

    49m <?php echo $alt; ?>
NOC Applied Time Series Analysis

MODULE 11

| 01h 01min total length
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    Live Session

    1h 61 min <?php echo $alt; ?>
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