What you will learn
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Chemical Engineering
The course introduces the concepts and methods of time-series analysis. Specifically, the topics include (i) stationarity and ergodicity (ii) auto-, cross- and partial-correlation functions (iii) linear random processes - definitions (iv) auto-regressive, moving average, ARIMA and seasonal ARIMA models (v) spectral (Fourier) analysis and periodicity detection and (vi) parameter estimation concepts and methods. Practical implementations in R are illustrated at each stage of the course. The subject of time-series analysis is of fundamental interest to data analysts in all fields of engineering, econometrics, climatology, humanities and medicine. Only few universities across the globe include this course on this topic despite its importance. This subject is foundational to all researchers interested in modelling uncertainties, developing models from data and multivariate data analysis.
Lecture01_Part1 - Motivation and Overview 1
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21:08
Lecture01_Part2 - Motivation and Overview
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16:31
Lecture02_Part1 - Motivation and Overview 3
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17:18
Lecture02_Part2 - Motivation and Overview 4
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22:10
Lecture03_Part1 - Motivation and Overview 5
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26:58
Lecture03_Part2 - Motivation and Overview 6
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26:56
Lecture04_Part1 - Probability and Statistics Review 1A
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25:44
Lecture04_Part2 - Probability and Statistics Review 1B
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25:05
Lecture05_Part1 - Probability and Statistics Review 1C
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25:51
Lecture05_Part2 - Probability and Statistics Review 1D
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24:25
Lecture06_Part1 - Probability and Statistics Review 2A
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22:04
Lecture06_Part2 - Probability and Statistics Review 2B
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14:28
Lecture06_Part3 - Probability and Statistics Review 2C
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14:13
Lecture07_Part1 - Probability and Statistics Review 2D
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25:15
Lecture07_Part2 - Probability and Statistics Review 2E
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29:13
Lecture07_Part3 - Probability and Statistics Review 2F
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12:44
Lecture08_Part1 - Probability and Statistics Review 2G (with R Demonstration)
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25:24
Lecture08_Part2 - Probability and Statistics Review 2H (with R Demonstration)
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31:25
Lecture9_Part1 - Probability and Statistics Review 2 I
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30:19
Lecture9_Part2 - Probability and Statistics Review 2J
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07:08
Lecture9_Part3 - Introduction to Random Processes 1
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28:56
Lecture10_Part1 - Introduction to Random Processes
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23:06
Lecture10_Part2 - Introduction to Random Processes 3
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26:37
Lecture11_Part1 - Introduction to Random Processes 4
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28:46
Lecture11_Part2 - Introduction to Random Processes 5
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15:23
Lecture11_Part3 - Autocovariance & Autocorrelation Functions 1
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06:07
Lecture12_Part1 - Autocovariance & Autocorrelation Functions 2
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29:23
Lecture12_Part2 - Autocovariance & Autocorrelation Functions 3
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25:26
Lecture13_Part1 - Autocovariance & Autocorrelation Functions 4
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24:36
Lecture13_Part2 - Autocovariance & Autocorrelation Functions 5
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13:22
Lecture13_Part3 - Autocovariance & Autocorrelation Functions 6
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23:52
Lecture14_Part1 - Autocovariance & Autocorrelation Functions 7
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28:46
Lecture14_Part2 - Autocovariance & Autocorrelation Functions 8
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24:41
Lecture15_Part1 - Autocovariance & Autocorrelation Functions 9
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25:01
Lecture15_Part2 - Partial Autocorrelation Functions
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29:11
Lecture16_Part1 - Autocorrelation and Partial-autocorrelation Functions (with R Demonstration)
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15:15
Lecture16_Part2 - Models for Linear Stationary Processes 1
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21:24
Lecture17_Part1 - Models for Linear Stationary Processes 2
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26:05
Lecture17_Part2 - Models for Linear Stationary Processes 3
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25:24
Lecture18_Part1 - Models for Linear Stationary Processes 4
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15:01
Lecture18_Part2 - Models for Linear Stationary Processes 5
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26:43
Lecture18_Part3 - Models for Linear Stationary Processes 6
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15:27
Lecture19_Part1 - Models for Linear Stationary Processes 7
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15:15
Lecture19_Part2 - Models for Linear Stationary Processes 8
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24:43
Lecture19_Part3 - Models for Linear Stationary Processes 9
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22:35
Lecture20_Part1 - Models for Linear Stationary Processes 10
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30:02
Lecture20_Part2 - Models for Linear Stationary Processes 11
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24:24
Lecture21_Part1 - Models for Linear Stationary Processes 12
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32:21
Lecture21_Part2 - Models for Linear Stationary Processes 13
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22:01
Lecture22_Part1 - Models for Linear Stationary Processes 14 (with R Demonstrations)
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30:56
Lecture22_Part2 - Models for Linear Stationary Processes 15 (with R Demonstrations)
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19:35
Lecture22_Part3 - Models for Linear Stationary Processes 16 (with R Demonstrations)
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17:46
Lecture23_Part1 - Models for Linear Non-stationary Processes 1
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23:55
Lecture23_Part2 - Models for Linear Non-stationary Processes 2 (with R Demonstrations)
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31:44
Lecture24_Part1 - Models for Linear Non-stationary Processes 3 (with R Demonstrations)
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28:57
Lecture24_Part2 - Models for Linear Non-stationary Processes 4
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14:52
Lecture25_Part1 - Models for Linear Non-stationary Processes 5
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21:17
Lecture25_Part2 - Models for Linear Non-stationary Processes 6 (with R Demonstrations)
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25:29
Lecture26_Part1 - Fourier Transforms for Deterministic Signals 1
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23:42
Lecture26_Part2 - Fourier Transforms for Deterministic Signals 2
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24:42
Lecture27_Part1 - Fourier Transforms for Deterministic Signals 3
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29:29
Lecture27_Part2 - Fourier Transforms for Deterministic Signals 4
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25:08
Lecture28_Part1 - Fourier Transforms for Deterministic Signals 5
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26:13
Lecture28_Part2 - Fourier Transforms for Deterministic Signals 6
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22:23
Lecture29_Part1 - Fourier Transforms for Deterministic Signals 7
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29:58
Lecture29_Part2 - Fourier Transforms for Deterministic Signals 8
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12:14
Lecture30_Part1 - Fourier Transforms for Deterministic Signals 9
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28:24
Lecture30_Part2 - DFT and Periodogram 1
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25:38
Lecture31_Part1 - DFT and Periodogram 2
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29:28
Lecture31_Part2 - DFT and Periodogram 3 (with R Demonstrations)
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25:08
Lecture32_Part1 - Spectral Representations of Random Processes 1
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22:29
Lecture32_Part2 - Spectral Representations of Random Processes 2
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30:29
Lecture33_Part1 - Spectral Representations of Random Processes 3
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28:08
Lecture33_Part2 - Spectral Representations of Random Processes 4
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13:30
Lecture33_Part3 - Spectral Representations of Random Processes 5
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13:11
Lecture34_Part1 - Spectral Representations of Random Processes 6
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28:56
Lecture34_Part2 - Spectral Representations of Random Processes 7
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33:16
Lecture35_Part1 - Introduction to Estimation Theory 1
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19:39
Lecture35_Part2 - Introduction to Estimation Theory 2
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27:39
Lecture35_Part3 - Introduction to Estimation Theory 3
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31:07
Lecture 36A - Introduction to Estimation Theory -4
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24:25
Lecture 36B - Goodness of Estimators 1 -1
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25:25
Lecture 37A - Goodness of Estimators 1 -2
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17:41
Lecture 37B - Goodness of Estimators 1 -3
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25:48
Lecture 37C - Goodness of Estimators 1 -4
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10:40
Lecture 38A - Goodness of Estimators 2 -1
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26:52
Lecture 38B - Goodness of Estimators 2 -2
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14:17
Lecture 38C - Goodness of Estimators 2 -3
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18:49
Lecture 39A - Goodness of Estimators 2 -4
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24:27
Lecture 39B - Goodness of Estimators 2 -5 (with R demonstrations)
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18:43
Lecture 39C - Goodness of Estimators 2 -6
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10:49
Lecture 40A - Goodness of Estimators 2 -7
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26:10
Lecture 40B - Goodness of Estimators 2 -8
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27:12
Lecture 41A - Estimation Methods 1 -1
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27:04
Lecture 41B - Estimation Methods 1 -2
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28:55
Lecture 42A - Estimation Methods 1 -3
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25:47
Lecture 42B - Estimation Methods 1 -4
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23:31
Lecture 42C - Estimation Methods 1 -5
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19:39
Lecture 43A - Estimation Methods 1 -6 (with R demonstrations)
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26:40
Lecture 43B - Estimation Methods 1 -7(with R demonstrations)
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26:57
Lecture 44A - Estimation Methods 1 -8
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23:04
Lecture 44B - Estimation Methods 1 -9
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18:29
Lecture 44C - Estimation Methods 2 -1
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08:13
Lecture 45A - Estimation Methods 2 -2
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27:45
Lecture 45B - Estimation Methods 2 -3
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20:35
Lecture 46A - MLE and Bayesian Estimation -1
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31:38
Lecture 46B - MLE and Bayesian Estimation -2
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14:18
Lecture 47A - MLE and Bayesian Estimation -3
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21:35
Lecture 47B - MLE and Bayesian Estimation -4
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21:12
Lecture 47B - MLE and Bayesian Estimation -4
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21:12
Lecture 48A - Estimation of Time Domain Statistics -1
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28:43
Lecture 48B - Estimation of Time Domain Statistics -2
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28:49
Lecture 49 - Periodogram as PSD Estimator
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49:12
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Meet Your Instructor
About Instructor
Prof. Arun K. Tangirala is a Professor in the Department of Chemical Engineering, IIT Madras. He specializes in process systems engineering with research in data-driven modelling, process control, system identification and sparse optimization. Dr. Tangirala has conducted several courses, workshops on time-series analysis, applied DSP and system identification over the last 12 years. He is the author of a widely appreciated classroom text on "Principles of System Identification: Theory and Practice"
Free
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Course Duration48 h 22 m 28 s
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Course LevelIntermediate
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Student Enrolled0
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LanguageEnglish
This Course Includes
- 48 h 22 m 28 s Video Lectures
- 0 Quizzes
- 0 Assignments
- 0 Downloadable Resources
- Full Lifetime Access
- Certificate of Completion