Applied Time-Series Analysis

By Prof. Arun K. Tangirala   |   IIT Madras
Learners enrolled: 986
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.

INTENDED AUDIENCE : Students, researchers and practitioners of data analysis from all disciplines of engineering, economics, humanities and medicine
PREREQUISITES         : Basics of probability and statistics; View MOOC videos on "Intro to Statistical Hypothesis Testing"
INDUSTRIES SUPPORT: Gramener, Honeywell, ABB, GyanData, GE, Ford, Siemens, and all companies that work on Data Analytics
Course Status : Completed
Course Type : Core
Duration : 12 weeks
Category :
  • Chemical Engineering
Credit Points : 3
Level : Postgraduate
Start Date : 24 Jan 2022
End Date : 15 Apr 2022
Enrollment Ends : 07 Feb 2022
Exam Date : 24 Apr 2022 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction & Overview; Review of Probability & Statistics – Parts 1 & 2
Week 2: Introduction to Random Processes; Stationarity & Ergodicity
Week 3: Auto- and cross-correlation functions; Partial correlation functions
Week 4: Linear random processes; Auto-regressive, Moving average and ARMA models
Week 5: Models for non-stationary processes; Trends, heteroskedasticity and ARIMA models
Week 6: Fourier analysis of deterministic signals; DFT and periodogram
Week 7: Spectral densities and representations; Wiener-Khinchin theorem; Harmonic processes; SARIMA models
Week 8: Introduction to estimation theory; Goodness of estimators; Fisher’s information
Week 9: Properties of estimators; bias, variance, efficiency; C-R bound; consistency
Week 10: Least squares, WLS and non-linear LS estimators
Week 11: Maximum likelihood and Bayesian estimators.
Week 12: Estimation of signal properties, time-series models; Case studies

Books and references


Instructor bio

Prof. Arun K. Tangirala

IIT Madras
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".

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 24 April 2022 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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