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 THAT WILL RECOGNIZE THIS COURSE
Gramener, Honeywell, ABB, GyanData, GE, Ford, Siemens, and all companies that work on Data Analytics
656 students have enrolled already!!
COURSE INSTRUCTOR
Prof. Arun K. Tangiralais 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 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
MORE DETAILS OF THE COURSE Name of the course: Applied Time-Series Analysis Course url:https://onlinecourses.nptel.ac.in/noc18_ch17/ Course duration : 12 weeks Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) : Morning session 9am to 12 noon; Afternoon session: 2pm to 5pm Exam for this course will be available in one session on both 28 and 29 April. The exact session it will be available in (FN/AN) - we shall inform by first week of January 2018. Registration url: Will be announced shortly
CERTIFICATION EXAM
The exam is optional for a fee.
Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) : Morning session 9am to 12 noon; Afternoon session: 2pm to 5pm
Exam for this course will be available in one session on both 28 and 29 April. The exact session it will be available in (FN/AN) - we shall inform by first week of January 2018.
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.
CERTIFICATE
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.
