Courses » Applied Time-Series Analysis

Applied Time-Series Analysis


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.


Students, researchers and practitioners of data analysis from all disciplines of engineering, economics, humanities and medicine


Basics of probability and statistics; View MOOC videos on "Intro to Statistical Hypothesis Testing"


Gramener, Honeywell, ABB, GyanData, GE, Ford, Siemens, and all companies that work on Data Analytics


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".


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


Name of the course:  Applied Time-Series Analysis
Course duration : 12 weeks (In weeks)
Dates of exams : 23 April 2017
  • Time of exam : Shift 1: 9am-12 noon; Shift 2: 2pm-5pm
  • Any one shift can be chosen to write the exam for a course.
Final List of exam cities will be available in exam registration form.
Exam registration url - Will be announced shortly

  • The exam is optional for a fee. Exams will be on 23 April 2017
  • Time: Shift 1: 9am-12 noon; Shift 2: 2pm-5pm (Any one shift can be chosen to write the exam for a course.)
  • 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.
  • Final score will be calculated as : 25% assignment score + 75% final exam score
  • 25% assignment score is calculated as 25% of average of Best 8 out of 12 assignments
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. 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.