Master Time Series Analysis & Forecasting in R | IIT Bombay Course
Course Details
| Exam Registration | 240 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering, Finance |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 18 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master Time Series Analysis: A 12-Week Journey from Fundamentals to Advanced Forecasting
In today's data-driven world, understanding patterns over time is crucial for decision-making across finance, climate science, business, and technology. The ability to model and forecast time series data is a highly sought-after skill. This comprehensive 12-week course, Time Series Modelling and Forecasting with Applications in R, offered by Prof. Sudeep Bapat of IIT Bombay, is designed to equip you with exactly that expertise.
About the Course Instructor: Prof. Sudeep R. Bapat
Learning from an expert is paramount. This course is led by Prof. Sudeep R. Bapat, an Assistant Professor at the Shailesh J. Mehta School of Management, IIT Bombay. With a rich academic background including a PhD from the University of Connecticut, USA, and teaching experience at institutions like UC Santa Barbara and IIM Indore, Prof. Bapat brings both depth and clarity to complex topics. His publication of over 40 research articles in statistical learning, time series, and change point detection ensures you are learning cutting-edge methodologies directly from an active contributor to the field.
Who Should Enroll in This Course?
This course is meticulously structured to cater to a wide audience, making advanced time series analysis accessible and practical.
- Intended Audience: Undergraduate and Postgraduate students (BSc, MSc, MBA, MTech, PhD) with an interest in statistics, analytics, finance, and data science.
- Prerequisites: A basic understanding of statistical inference and regression is recommended to get the most out of the curriculum.
- Industry Support & Applications: The skills taught are directly applicable in financial institutions, banks, insurance companies, climatology, economics, and sales analytics. Professionals at organizations like SBI, HDFC, ICICI, and ICICI Prudential will find immediate use for these forecasting techniques.
Detailed 12-Week Course Curriculum
The course is a progressive journey from foundational concepts to sophisticated modelling techniques, all with hands-on application in R.
| Week | Topics Covered |
|---|---|
| Week 1-2 | Introduction to time series, stationarity, decomposition, and basic models (Random Walk, White Noise, AR, MA, ARMA). Introduction to ACF/PACF plots for model identification. |
| Week 3-4 | Tests for stationarity (ADF), handling non-stationarity with ARIMA/SARIMA, model estimation, and diagnostic checking (Ljung-Box test). |
| Week 5 | Forecasting methods: ARIMA, Simple Moving Average (SMA), Exponential Smoothing (EMA), Holt-Winters. Comparing forecast accuracy. |
| Week 6 | Advanced models: Fractionally integrated processes (ARFIMA) and long-memory properties. |
| Week 7-8 | Multivariate Time Series: Vector Autoregression (VAR, VARMA), cointegration, error correction models, and causality tests (Granger causality). |
| Week 9 | Spectral Analysis: Fourier transformation, frequency domain analysis, and spectral density. |
| Week 10 | Volatility Modelling: Introduction to ARCH and GARCH models for financial time series. |
| Week 11 | Non-linear Time Series: Threshold Autoregressive (TAR), Smooth Transition Autoregressive (STAR), and Markov switching models. |
| Week 12 | Machine Learning for Time Series: Anomaly detection, Long Short-Term Memory (LSTM) networks, and neural networks. |
Why Learn Time Series Analysis with R?
R is the premier language for statistical computing and graphics. This course emphasizes hands-on applications and exercises in R, ensuring you not only understand the theory but also know how to implement it. You will learn to build, diagnose, and forecast using real-world datasets, translating academic knowledge into practical, job-ready skills.
Key Textbooks and References
To supplement the video lectures and R labs, the course aligns with seminal texts in the field:
- Shumway & Stoffer, Time Series Analysis and Its Applications
- Brockwell & Davis, Introduction to Time Series Forecasting
- Tsay, Analysis of Financial Time Series
Whether you aim to forecast stock prices, predict climate patterns, optimize business sales, or detect anomalies in sensor data, this course provides the complete toolkit. Enroll today to start your journey in mastering one of the most powerful forms of data analysis.
Enroll Now →