Applied Time-Series Analysis Course | IIT Madras | Prof. Arun K. Tangirala
Course Details
| Exam Registration | 43 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 12 weeks |
| Categories | Chemical Engineering |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 17 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Unlock the Power of Data with Applied Time-Series Analysis
In today's data-driven world, the ability to analyze and interpret sequential data is a superpower. From forecasting stock prices and economic trends to monitoring industrial processes and climate patterns, time-series analysis forms the backbone of critical decision-making across sectors. Recognizing this need, the prestigious Indian Institute of Technology Madras (IIT Madras) offers a profound 12-week postgraduate course on Applied Time-Series Analysis, meticulously designed and delivered by Prof. Arun K. Tangirala.
Why is This Course a Must for Data Professionals?
Time-series data is ubiquitous, yet its analysis requires specialized statistical tools and deep conceptual understanding. This course bridges the gap between theoretical foundations and practical application. It is tailored for students, researchers, and practitioners who aim to move beyond basic data analysis to model uncertainties, develop predictive models from temporal data, and perform sophisticated multivariate analysis. Given its foundational importance, proficiency in time-series analysis is highly sought after in industries ranging from finance and automation to healthcare and climate science.
Meet Your Instructor: Prof. Arun K. Tangirala
The course is led by Prof. Arun K. Tangirala, a distinguished Professor in the Department of Chemical Engineering at IIT Madras. A specialist in process systems engineering, his research spans data-driven modelling, process control, system identification, and sparse optimization. With over 12 years of experience conducting courses and workshops on time-series analysis and applied DSP, Prof. Tangirala brings unparalleled expertise to the classroom. He is also the author of the acclaimed textbook "Principles of System Identification: Theory and Practice," ensuring that the course content is both rigorous and pedagogically sound.
Course Overview and Structure
This intensive 12-week program is structured to take you from fundamental concepts to advanced estimation techniques, with practical implementation at every step.
Detailed 12-Week Course Layout
| Week | Topics Covered |
|---|---|
| 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 |
Key Learning Outcomes
By the end of this course, you will have a strong command over:
- Core Concepts: Deep understanding of stationarity, ergodicity, and correlation functions.
- Modelling Expertise: Ability to build, diagnose, and forecast using AR, MA, ARMA, ARIMA, and Seasonal ARIMA (SARIMA) models.
- Spectral Analysis: Skills to perform Fourier analysis and detect periodicities in data using spectral densities.
- Statistical Rigor: Knowledge of estimation theory, including properties of estimators, Maximum Likelihood Estimation (MLE), and Bayesian methods.
- Practical Implementation: Hands-on experience implementing all concepts using the R programming language, making you job-ready.
Who Should Enroll?
INTENDED AUDIENCE: This course is ideal for:
- Postgraduate students and researchers in Engineering, Economics, Climatology, Humanities, and Medicine.
- Data Analysts, Scientists, and Practitioners looking to add time-series modelling to their skill set.
- Professionals in industries that rely on forecasting, predictive maintenance, and signal processing.
Prerequisites
To ensure you get the most out of this advanced course, a foundation in the following is recommended:
- Basics of Probability Theory and Linear Algebra
- Fundamentals of Optimization
- Familiarity with Statistical Hypothesis Testing (MOOC videos are suggested)
Industry Relevance and Support
The skills taught in this course are directly applicable and highly valued in the industry. Leading companies working on Data Analytics, including Gramener, Honeywell, ABB, GyanData, GE, Ford, and Siemens, recognize the importance of time-series analysis. Mastery of this subject opens doors to roles in data science, quantitative analysis, process optimization, and research & development in these top-tier organizations.
Take the Next Step in Your Data Analytics Journey
"Applied Time-Series Analysis" from IIT Madras is more than just a course; it's an investment in a critical skill set that will distinguish you in the competitive field of data analytics. Under the expert guidance of Prof. Tangirala, you will gain the theoretical knowledge and practical confidence to tackle real-world temporal data challenges. Enroll today and start transforming raw data into actionable insights and robust forecasts.
Enroll Now →