NOC:Applied Time-Series Analysis (USB)

Special Price ₹1,000.00 Regular Price ₹1,250.00
In stock
SKU
103106123



Media Storage Type : 64 GB USB Stick

NPTEL Subject Matter Expert : Dr. Arun K.Tangirala

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 112

NPTEL Course Size : 30 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

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

Write Your Own Review
You're reviewing:NOC:Applied Time-Series Analysis (USB)
Copyright © 2019 LinuXpert Systems, Chennai. All rights reserved.