NPTEL : NOC:Applied Time-Series Analysis (Chemical Engineering)

Co-ordinators : Dr. Arun K.Tangirala


Lecture 1 - Lecture 1 - Part 1 - Motivation and Overview 1

Lecture 2 - Lecture 1 - Part 2 - Motivation and Overview 2

Lecture 3 - Lecture 2 - Part 1 - Motivation and Overview 3

Lecture 4 - Lecture 2 - Part 2 - Motivation and Overview 4

Lecture 5 - Lecture 3 - Part 1 - Motivation and Overview 5

Lecture 6 - Lecture 3 - Part 2 - Motivation and Overview 6

Lecture 7 - Lecture 4 - Part 1 - Probability and Statistics Review 1A

Lecture 8 - Lecture 4 - Part 2 - Probability and Statistics Review 1B

Lecture 9 - Lecture 5 - Part 1 - Probability and Statistics Review 1C

Lecture 10 - Lecture 5 - Part 2 - Probability and Statistics Review 1D

Lecture 11 - Lecture 6 - Part 1 - Probability and Statistics Review 2A

Lecture 12 - Lecture 6 - Part 2 - Probability and Statistics Review 2B

Lecture 13 - Lecture 6 - Part 3 - Probability and Statistics Review 2C

Lecture 14 - Lecture 7 - Part 1 - Probability and Statistics Review 2D

Lecture 15 - Lecture 7 - Part 2 - Probability and Statistics Review 2E

Lecture 16 - Lecture 7 - Part 3 - Probability and Statistics Review 2F

Lecture 17 - Lecture 8 - Part 1 - Probability and Statistics Review 2G (with R Demonstration)

Lecture 18 - Lecture 8 - Part 2 - Probability and Statistics Review 2H (with R Demonstration)

Lecture 19 - Lecture 9 - Part 1 - Probability and Statistics Review 2I

Lecture 20 - Lecture 9 - Part 2 - Probability and Statistics Review 2J

Lecture 21 - Lecture 9 - Part 3 - Introduction to Random Processes 1

Lecture 22 - Lecture 10 - Part 1 - Introduction to Random Processes 2

Lecture 23 - Lecture 10 - Part 2 - Introduction to Random Processes 3

Lecture 24 - Lecture 11 - Part 1 - Introduction to Random Processes 4

Lecture 25 - Lecture 11 - Part 2 - Introduction to Random Processes 5

Lecture 26 - Lecture 11 - Part 3 - Autocovariance & Autocorrelation Functions 1

Lecture 27 - Lecture 12 - Part 1 - Autocovariance & Autocorrelation Functions 2

Lecture 28 - Lecture 12 - Part 2 - Autocovariance & Autocorrelation Functions 3

Lecture 29 - Lecture 13 - Part 1 - Autocovariance & Autocorrelation Functions 4

Lecture 30 - Lecture 13 - Part 2 - Autocovariance & Autocorrelation Functions 5

Lecture 31 - Lecture 13 - Part 3 - Autocovariance & Autocorrelation Functions 6

Lecture 32 - Lecture 14 - Part 1 - Autocovariance & Autocorrelation Functions 7

Lecture 33 - Lecture 14 - Part 2 - Autocovariance & Autocorrelation Functions 8

Lecture 34 - Lecture 15 - Part 1 - Autocovariance & Autocorrelation Functions 9

Lecture 35 - Lecture 15 - Part 2 - Partial Autocorrelation Functions

Lecture 36 - Lecture 16 - Part 1 - Autocorrelation and Partial-autocorrelation Functions (with R Demonstration)

Lecture 37 - Lecture 16 - Part 2 - Models for Linear Stationary Processes 1

Lecture 38 - Lecture 17 - Part 1 - Models for Linear Stationary Processes 2

Lecture 39 - Lecture 17 - Part 2 - Models for Linear Stationary Processes 3

Lecture 40 - Lecture 18 - Part 1 - Models for Linear Stationary Processes 4

Lecture 41 - Lecture 18 - Part 2 - Models for Linear Stationary Processes 5

Lecture 42 - Lecture 18 - Part 3 - Models for Linear Stationary Processes 6

Lecture 43 - Lecture 19 - Part 1 - Models for Linear Stationary Processes 7

Lecture 44 - Lecture 19 - Part 2 - Models for Linear Stationary Processes 8

Lecture 45 - Lecture 19 - Part 3 - Models for Linear Stationary Processes 9

Lecture 46 - Lecture 20 - Part 1 - Models for Linear Stationary Processes 10

Lecture 47 - Lecture 20 - Part 2 - Models for Linear Stationary Processes 11

Lecture 48 - Lecture 21 - Part 1 - Models for Linear Stationary Processes 12

Lecture 49 - Lecture 21 - Part 2 - Models for Linear Stationary Processes 13

Lecture 50 - Lecture 22 - Part 1 - Models for Linear Stationary Processes 14 (with R Demonstrations)

Lecture 51 - Lecture 22 - Part 2 - Models for Linear Stationary Processes 15 (with R Demonstrations)

Lecture 52 - Lecture 22 - Part 3 - Models for Linear Stationary Processes 16 (with R Demonstrations)

Lecture 53 - Lecture 23 - Part 1 - Models for Linear Non-stationary Processes 1

Lecture 54 - Lecture 23 - Part 2 - Models for Linear Non-stationary Processes 2 (with R Demonstrations)

Lecture 55 - Lecture 24 - Part 1 - Models for Linear Non-stationary Processes 3 (with R Demonstrations)

Lecture 56 - Lecture 24 - Part 2 - Models for Linear Non-stationary Processes 4

Lecture 57 - Lecture 25 - Part 1 - Models for Linear Non-stationary Processes 5

Lecture 58 - Lecture 25 - Part 2 - Models for Linear Non-stationary Processes 6 (with R Demonstrations)

Lecture 59 - Lecture 26 - Part 1 - Fourier Transforms for Deterministic Signals 1

Lecture 60 - Lecture 26 - Part 2 - Fourier Transforms for Deterministic Signals 2

Lecture 61 - Lecture 27 - Part 1 - Fourier Transforms for Deterministic Signals 3

Lecture 62 - Lecture 27 - Part 2 - Fourier Transforms for Deterministic Signals 4

Lecture 63 - Lecture 28 - Part 1 - Fourier Transforms for Deterministic Signals 5

Lecture 64 - Lecture 28 - Part 2 - Fourier Transforms for Deterministic Signals 6

Lecture 65 - Lecture 29 - Part 1 - Fourier Transforms for Deterministic Signals 7

Lecture 66 - Lecture 29 - Part 2 - Fourier Transforms for Deterministic Signals 8

Lecture 67 - Lecture 30 - Part 1 - Fourier Transforms for Deterministic Signals 9

Lecture 68 - Lecture 30 - Part 2 - DFT and Periodogram 1

Lecture 69 - Lecture 31 - Part 1 - DFT and Periodogram 2

Lecture 70 - Lecture 31 - Part 2 - DFT and Periodogram 3 (with R Demonstrations)

Lecture 71 - Lecture 32 - Part 1 - Spectral Representations of Random Processes 1

Lecture 72 - Lecture 32 - Part 2 - Spectral Representations of Random Processes 2

Lecture 73 - Lecture 33 - Part 1 - Spectral Representations of Random Processes 3

Lecture 74 - Lecture 33 - Part 2 - Spectral Representations of Random Processes 4

Lecture 75 - Lecture 33 - Part 3 - Spectral Representations of Random Processes 5

Lecture 76 - Lecture 34 - Part 1 - Spectral Representations of Random Processes 6

Lecture 77 - Lecture 34 - Part 2 - Spectral Representations of Random Processes 7

Lecture 78 - Lecture 35 - Part 1 - Introduction to Estimation Theory 1

Lecture 79 - Lecture 35 - Part 2 - Introduction to Estimation Theory 2

Lecture 80 - Lecture 35 - Part 3 - Introduction to Estimation Theory 3

Lecture 81 - Lecture 36A - Introduction to Estimation Theory -4

Lecture 82 - Lecture 36B - Goodness of Estimators 1 - 1

Lecture 83 - Lecture 37A - Goodness of Estimators 1 - 2

Lecture 84 - Lecture 37B - Goodness of Estimators 1 - 3

Lecture 85 - Lecture 37C - Goodness of Estimators 1 - 4

Lecture 86 - Lecture 38A - Goodness of Estimators 2 - 1

Lecture 87 - Lecture 38B - Goodness of Estimators 2 - 2

Lecture 88 - Lecture 38C - Goodness of Estimators 2 - 3

Lecture 89 - Lecture 39A - Goodness of Estimators 2 - 4

Lecture 90 - Lecture 39B - Goodness of Estimators 2 - 5 (with R demonstrations)

Lecture 91 - Lecture 39C - Goodness of Estimators 2 - 6

Lecture 92 - Lecture 40A - Goodness of Estimators 2 - 7

Lecture 93 - Lecture 40B - Goodness of Estimators 2 - 8

Lecture 94 - Lecture 41A - Estimation Methods 1 - 1

Lecture 95 - Lecture 41B - Estimation Methods 1 - 2

Lecture 96 - Lecture 42A - Estimation Methods 1 - 3

Lecture 97 - Lecture 42B - Estimation Methods 1 - 4

Lecture 98 - Lecture 42C - Estimation Methods 1 - 5

Lecture 99 - Lecture 43A - Estimation Methods 1 - 6 (with R demonstrations)

Lecture 100 - Lecture 43B - Estimation Methods 1 - 7 (with R demonstrations)

Lecture 101 - Lecture 44A - Estimation Methods 1 - 8

Lecture 102 - Lecture 44B - Estimation Methods 1 - 9

Lecture 103 - Lecture 44C - Estimation Methods 2 - 1

Lecture 104 - Lecture 45A - Estimation Methods 2 - 2

Lecture 105 - Lecture 45B - Estimation Methods 2 - 3

Lecture 106 - Lecture 46A - MLE and Bayesian Estimation - 1

Lecture 107 - Lecture 46B - MLE and Bayesian Estimation - 2

Lecture 108 - Lecture 47A - MLE and Bayesian Estimation - 3

Lecture 109 - Lecture 47B - MLE and Bayesian Estimation - 4

Lecture 110 - Lecture 48A - Estimation of Time Domain Statistics - 1

Lecture 111 - Lecture 48B - Estimation of Time Domain Statistics - 2

Lecture 112 - Lecture 49 - Periodogram as PSD Estimator