Course103106123120 lectures
Applied Time-Series Analysis
Prof. Arun K Tangirala · IIT Madras
Lectures
Week 1: Introduction & Overview
- Lecture 01A: Motivation and Overview-1
- Lecture 01B: Motivation and Overview-2
- Lecture 02A: Motivation and Overview-3
- Lecture 02B: Motivation and Overview-4
- Lecture 03A: Motivation and Overview-5
- Lecture 03B: Motivation and Overview-6
- Lecture 04A: Probability and Statistics Review (Part 1)-1
- Lecture 04B: Probability and Statistics Review (Part 1)-2
- Lecture 05A: Probability and Statistics Review (Part 1)-3
- Lecture 05B: Probability and Statistics Review (Part 1)-4
Week 2: Review of Probability & Statistics
- Lecture 06A: Probability and Statistics Review (Part 2)-1
- Lecture 06B: Probability and Statistics Review (Part 2)-2
- Lecture 06C: Probability and Statistics Review (Part 2)-3
- Lecture 07A: Probability and Statistics Review (Part 2)-4
- Lecture 07B: Probability and Statistics Review (Part 2)-5
- Lecture 07C: Probability and Statistics Review (Part 2)-6
- Lecture 08A: Probability and Statistics Review (Part 2)-7
- Lecture 08B: Probability and Statistics Review (Part 2)-8
- Lecture 09A: Probability and Statistics Review (Part 2)-9
- Lecture 09B: Probability and Statistics Review (Part 2)-10
- Lecture 09C: Introduction to Random Processes-1
Week 3: Introduction to Random Processes, Auto- and Cross- Correlation Functions
- Lecture 10A: Introduction to Random Processes-2
- Lecture 10B: Introduction to Random Processes-3
- Lecture 11A: Introduction to Random Processes-4
- Lecture 11B: Introduction to Random Processes-5
- Lecture 11C: Autocovariance & Autocorrelation Functions-1
- Lecture 12A: Autocovariance & Autocorrelation Functions-2
- Lecture 12B: Autocovariance & Autocorrelation Functions-3
- Lecture 13A: Autocovariance & Autocorrelation Functions-4
- Lecture 13B: Autocovariance & Autocorrelation Functions-5
- Lecture 13C: Autocovariance & Autocorrelation Functions-6
- Lecture 14A: Autocovariance & Autocorrelation Functions-7
Week 4: Auto- and cross-correlation functions (contd.), Models for Linear Stationary Processes
- Lecture 14B: Autocovariance & Autocorrelation Functions-8
- Lecture 15A: Autocovariance & Autocorrelation Functions-9
- Lecture 15B: Partial Autocorrelation Functions
- Lecture 16A: Autocorrelation and Partial autocorrelation Functions with R Demonstration
- Lecture 16B: Models for Linear Stationary Processes-1
- Lecture 17A: Models for Linear Stationary Processes-2
- Lecture 17B: Models for Linear Stationary Processes-3
- Lecture 18A: Models for Linear Stationary Processes-4
- Lecture 18B: Models for Linear Stationary Processes-5
- Lecture 18C: Models for Linear Stationary Processes-6
- Lecture 19A: Models for Linear Stationary Processes-7
Week 5: Models for Linear Stationary & Non-Stationary Processes
- Lecture 19B: Models for Linear Stationary Processes-8
- Lecture 19C: Models for Linear Stationary Processes-9
- Lecture 20A: Models for Linear Stationary Processes -10
- Lecture 20B: Models for Linear Stationary Processes -11
- Lecture 21A: Models for Linear Stationary Processes -12
- Lecture 21B: Models for Linear Stationary Processes -13
- Lecture 22A: Models for Linear Stationary Processes -14 (with R Demonstrations)
- Lecture 22B: Models for Linear Stationary Processes -15 (with R Demonstrations)
- Lecture 22C: Models for Linear Stationary Processes -16 ( with R Demonstrations)
- Lecture 23A: Models for Linear Non stationary Processes -1
Week 6: Models for Linear Non-Stationary Processes (contd.), Fourier Transforms
- Lecture 23B: Models for Linear Non stationary Processes -2 (with R Demonstrations)
- Lecture 24A: Models for Linear Non stationary Processes -3 (with R Demonstrations)
- Lecture 24B: Models for Linear Non stationary Processes -4
- Lecture 25A: Models for Linear Non stationary Processes -5
- Lecture 25B: Models for Linear Non stationary Processes -6 (with R Demonstrations)
- Lecture 26A: Fourier Transforms for Deterministic Signals -1
- Lecture 26B: Fourier Transforms for Deterministic Signals -2
- Lecture 27A: Fourier Transforms for Deterministic Signals -3
- Lecture 27B: Fourier Transforms for Deterministic Signals -4
- Lecture 28A: Fourier Transforms for Deterministic Signals -5
Week 7: Fourier Transforms, DFT and Periodogram
- Lecture 28B: Fourier Transforms for Deterministic Signals -6
- Lecture 29A: Fourier Transforms for Deterministic Signals -7
- Lecture 29B: Fourier Transforms for Deterministic Signals -8
- Lecture 30A: Fourier Transforms for Deterministic Signals -9
- Lecture 30B: DFT and Periodogram -1
- Lecture 31A: DFT and Periodogram -2
- Lecture 31B: DFT and Periodogram -3 (with R Demonstrations)
- Lecture 32A: Spectral Representations of Random Processes -1
- Lecture 32B: Spectral Representations of Random Processes -2
- Lecture 33A: Spectral Representations of Random Processes -3
Week 8: Spectral Representations & Estimation Theory
- Lecture 33B: Spectral Representations of Random Processes -4
- Lecture 33C: Spectral Representations of Random Processes -5
- Lecture 34A: Spectral Representations of Random Processes -6
- Lecture 34B: Spectral Representations of Random Processes -7
- Lecture 35A: Introduction to Estimation Theory -1
- Lecture 35B: Introduction to Estimation Theory -2
- Lecture 35C: Introduction to Estimation Theory -3
- Lecture 36A: Introduction to Estimation Theory 4
- Lecture 36B: Goodness of Estimators 1 -1
- Lecture 37A: Goodness of Estimators 1 -2
- Lecture 37B: Goodness of Estimators 1 -3
Week 9: Estimation Theory
- Lecture 37C: Goodness of Estimators 1 -4
- Lecture 38A: Goodness of Estimators 2 -1
- Lecture 38B: Goodness of Estimators 2 -2
- Lecture 38C: Goodness of Estimators 2 -3
- Lecture 39A: Goodness of Estimators 2 -4
- Lecture 39B: Goodness of Estimators 2 -5 with R demonstrations
- Lecture 39C: Goodness of Estimators 2 -6
- Lecture 40A: Goodness of Estimators 2 -7
- Lecture 40B: Goodness of Estimators 2 -8
- Lecture 41A: Estimation Methods 1 -1
- Lecture 41B : Estimation Methods 1 -2
- Lecture 42A: Estimation Methods 1 -3
Week 10: Estimation Methods
- Lecture 42B: Estimation Methods 1 -4
- Lecture 42C: Estimation Methods 1 -5
- Lecture 43A: Estimation Methods 1 -6 with R demonstrations
- Lecture 43B: Estimation Methods 1 -7 with R demonstrations
- Lecture 44A: Estimation Methods 1 -8
- Lecture 44B: Estimation Methods 1 -9
- Lecture 44C: Estimation Methods 2 -1
- Lecture 45A: Estimation Methods 2 -2
- Lecture 45B: Estimation Methods 2 -3
- Lecture 46A: MLE and Bayesian Estimation -1
- Lecture 46B: MLE and Bayesian Estimation -2
Week 11: Estimation methods (contd.)
- Lecture 47A: MLE and Bayesian Estimation -3
- Lecture 47B: MLE and Bayesian Estimation -4
- Lecture 48A: Estimation of Time Domain Statistics -1
- Lecture 48B: Estimation of Time Domain Statistics -2
- Lecture 49: Periodogram as PSD Estimator
Week 12: Estimation of Power Spectral Density & Time Series Models
- Lecture 50A: Estimation of Power Spectral Density -1
- Lecture 50B: Estimation of Power Spectral Density -2
- Lecture 50C: Estimation of Power Spectral Density -3
- Lecture 50D: Estimation of Time Series Models -1
- Lecture 50E: Estimation of Time Series Models -2
Live Session
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Madras
- Department
- Chemical Engineering
- Teacher
- Prof. Arun K Tangirala
- Level
- Undergraduate
- Language
- English
- Subjects
- Chemistry, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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