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The [course_title] is intended to set you up with the concepts and methods of time-series analysis in an efficient manner. The subject of time-series analysis generally represents the fundamental interest of data analysts in all fields, such as engineering, econometrics, climatology, humanities and medicine. This course aimed at teaching you the statistical methods for the analysis of data and information. A variety of topics included, such as stationarity and ergodicity, auto, cross and partial-correlation functions, linear random processes and more.

### Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

### Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: NPTEL

### Course Curriculum

 Lecture 01A: Motivation and Overview-1 00:21:00 Lecture 01B: Motivation and Overview-2 00:17:00 Lecture 02A: Motivation and Overview-3 00:17:00 Lecture 02B: Motivation and Overview-4 00:22:00 Lecture 03A: Motivation and Overview-5 00:27:00 Lecture 03B: Motivation and Overview-6 00:27:00 Lecture 04A: Probability and Statistics Review (Part 1)-1 00:26:00 Lecture 04B: Probability and Statistics Review (Part 1)-2 00:25:00 Lecture 05A: Probability and Statistics Review (Part 1)-3 00:26:00 Lecture 05B: Probability and Statistics Review (Part 1)-4 00:25:00 Lecture 06A: Probability and Statistics Review (Part 2)-1 00:22:00 Lecture 06B: Probability and Statistics Review (Part 2)-2 00:15:00 Lecture 06C: Probability and Statistics Review (Part 2)-3 00:14:00 Lecture 07A: Probability and Statistics Review (Part 2)-4 00:25:00 Lecture 07B: Probability and Statistics Review (Part 2)-5 00:29:00 Lecture 07C: Probability and Statistics Review (Part 2)-6 00:13:00 Lecture 08A: Probability and Statistics Review (Part 2)-7 00:26:00 Lecture 08B: Probability and Statistics Review (Part 2)-8 00:32:00 Lecture 09A: Probability and Statistics Review (Part 2)-9 00:30:00 Lecture 09B: Probability and Statistics Review (Part 2)-10 00:07:00 Lecture 09C: Introduction to Random Processes-1 00:29:00 Lecture 10A: Introduction to Random Processes-2 00:23:00 Lecture 10B: Introduction to Random Processes-3 00:27:00 Lecture 11A: Introduction to Random Processes-4 00:29:00 Lecture 11B: Introduction to Random Processes-5 00:16:00 Lecture 11C: Autocovariance & Autocorrelation Functions-1 00:06:00 Lecture 12A: Autocovariance & Autocorrelation Functions-2 00:30:00 Lecture 12B: Autocovariance & Autocorrelation Functions-3 00:26:00 Lecture 13A: Autocovariance & Autocorrelation Functions-4 00:25:00 Lecture 13B: Autocovariance & Autocorrelation Functions-5 00:13:00 Lecture 13C: Autocovariance & Autocorrelation Functions-6 00:24:00 Lecture 14A: Autocovariance & Autocorrelation Functions-7 00:29:00 Lecture 14B: Autocovariance & Autocorrelation Functions-8 00:25:00 Lecture 15A: Autocovariance & Autocorrelation Functions-9 00:25:00 Lecture 15B: Partial Autocorrelation Functions 00:29:00 Lecture 16A: Autocorrelation and Partial autocorrelation Functions with R Demonstration 00:15:00 Lecture 16B: Models for Linear Stationary Processes-1 00:22:00 Lecture 17A: Models for Linear Stationary Processes-2 00:26:00 Lecture 17B: Models for Linear Stationary Processes-3 00:26:00 Lecture 18A: Models for Linear Stationary Processes-4 00:15:00 Lecture 18B: Models for Linear Stationary Processes-5 00:27:00 Lecture 18C: Models for Linear Stationary Processes-6 00:16:00 Lecture 19A: Models for Linear Stationary Processes-7 00:15:00 Lecture 19B: Models for Linear Stationary Processes-8 00:26:00 Lecture 19C: Models for Linear Stationary Processes-9 00:23:00 Lecture 20A: Models for Linear Stationary Processes -10 00:30:00 Lecture 20B: Models for Linear Stationary Processes -11 00:25:00 Lecture 21A: Models for Linear Stationary Processes -12 00:33:00 Lecture 21B: Models for Linear Stationary Processes -13 00:22:00 Lecture 22A: Models for Linear Stationary Processes -14 (with R Demonstrations) 00:31:00 Lecture 22B: Models for Linear Stationary Processes -15 (with R Demonstrations) 00:20:00 Lecture 22C: Models for Linear Stationary Processes -16 ( with R Demonstrations) 00:18:00 Lecture 23A: Models for Linear Non stationary Processes -1 00:24:00 Lecture 23B: Models for Linear Non stationary Processes -2 (with R Demonstrations) 00:32:00 Lecture 24A: Models for Linear Non stationary Processes -3 (with R Demonstrations) 00:29:00 Lecture 24B: Models for Linear Non stationary Processes -4 00:15:00 Lecture 25A: Models for Linear Non stationary Processes -5 00:21:00 Lecture 25B: Models for Linear Non stationary Processes -6 (with R Demonstrations) 00:26:00 Lecture 26A: Fourier Transforms for Deterministic Signals -1 00:24:00 Lecture 26B: Fourier Transforms for Deterministic Signals -2 00:25:00 Lecture 27A: Fourier Transforms for Deterministic Signals -3 00:30:00 Lecture 27B: Fourier Transforms for Deterministic Signals -4 00:25:00 Lecture 28A: Fourier Transforms for Deterministic Signals -5 00:26:00 Lecture 28B: Fourier Transforms for Deterministic Signals -6 00:23:00 Lecture 29A: Fourier Transforms for Deterministic Signals -7 00:30:00 Lecture 29B: Fourier Transforms for Deterministic Signals -8 00:12:00 Lecture 30A: Fourier Transforms for Deterministic Signals -9 00:29:00 Lecture 30B: DFT and Periodogram -1 00:26:00 Lecture 31A: DFT and Periodogram -2 00:30:00 Lecture 31B: DFT and Periodogram -3 (with R Demonstrations) 00:25:00 Lecture 32A: Spectral Representations of Random Processes -1 00:23:00 Lecture 32B: Spectral Representations of Random Processes -2 00:31:00 Lecture 33A: Spectral Representations of Random Processes -3 00:28:00 Lecture 33B: Spectral Representations of Random Processes -4 00:14:00 Lecture 33C: Spectral Representations of Random Processes -5 00:13:00 Lecture 34A: Spectral Representations of Random Processes -6 00:29:00 Lecture 34B: Spectral Representations of Random Processes -7 00:33:00 Lecture 35A: Introduction to Estimation Theory -1 00:20:00 Lecture 35B: Introduction to Estimation Theory -2 00:28:00 Lecture 35C: Introduction to Estimation Theory -3 00:31:00 Lecture 36A: Introduction to Estimation Theory 4 00:25:00 Lecture 36B: Goodness of Estimators 1 -1 00:26:00 Lecture 37A: Goodness of Estimators 1 -2 00:18:00 Lecture 37B: Goodness of Estimators 1 -3 00:26:00 Lecture 37C: Goodness of Estimators 1 -4 00:11:00 Lecture 38A: Goodness of Estimators 2 -1 00:27:00 Lecture 38B: Goodness of Estimators 2 -2 00:14:00 Lecture 38C: Goodness of Estimators 2 -3 00:19:00 Lecture 39A: Goodness of Estimators 2 -4 00:25:00 Lecture 39B: Goodness of Estimators 2 -5 with R demonstrations 00:19:00 Lecture 39C: Goodness of Estimators 2 -6 00:11:00 Lecture 40A: Goodness of Estimators 2 -7 00:26:00 Lecture 40B: Goodness of Estimators 2 -8 00:27:00 Lecture 41A: Estimation Methods 1 -1 00:27:00 Lecture 41B : Estimation Methods 1 -2 00:29:00 Lecture 42A: Estimation Methods 1 -3 00:26:00 Lecture 42B: Estimation Methods 1 -4 00:24:00 Lecture 42C: Estimation Methods 1 -5 00:20:00 Lecture 43A: Estimation Methods 1 -6 with R demonstrations 00:27:00 Lecture 43B: Estimation Methods 1 -7 with R demonstrations 00:27:00 Lecture 44A: Estimation Methods 1 -8 00:23:00 Lecture 44B: Estimation Methods 1 -9 00:19:00 Lecture 44C: Estimation Methods 2 -1 00:08:00 Lecture 45A: Estimation Methods 2 -2 00:28:00 Lecture 45B: Estimation Methods 2 -3 00:21:00 Lecture 46A: MLE and Bayesian Estimation -1 00:32:00 Lecture 46B: MLE and Bayesian Estimation -2 00:14:00 Lecture 47A: MLE and Bayesian Estimation -3 00:22:00 Lecture 47B: MLE and Bayesian Estimation -4 00:21:00 Lecture 48A: Estimation of Time Domain Statistics -1 00:29:00 Lecture 48B: Estimation of Time Domain Statistics -2 00:29:00 Lecture 49: Periodogram as PSD Estimator 00:49:00 Assessment Submit Your Assignment 00:00:00 Certification 00:00:00

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