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Time Series Modelling and Forecasting with Applications in R

Prof. Sudeep Bapat · IIT Bombay

Start with lecture 1 At NPTEL

Lectures

week-01

  1. Week 01: Lecture 01: Time series introduction Video
  2. Week 01: Lecture 02: Examples of time series data Video
  3. Week 01: Lecture 03: Stationarity in time series Video
  4. Week 01: Lecture 04: Weak vs. strong stationarity Video
  5. Week 01: Lecture 05: Practical session in R - 1 Video

week-02

  1. Week 02: Lecture 06: Time Series Decomposition Video
  2. Week 02: Lecture 07: Basic Time Series Processes Video
  3. Week 02: Lecture 08: Autocorrelation and the Partial Autocorrelation Functions Video
  4. Week 02: Lecture 09: ACF and PACF for Some Time Series Processes Video
  5. Week 02: Lecture 10: Practical Session in R-2 Video

week-03

  1. Week 03: Lecture 11: Non-Stationary Time Series Video
  2. Week 03: Lecture 12: Seasonality and its Features Video
  3. Week 03: Lecture 13: Cyclicality and Test for Stationarity Video
  4. Week 03: Lecture 14: Seasonality and SARIMA Model Video
  5. Week 03: Lecture 15: Practical Session in R-3 Video

week-04

  1. Week 04: Lecture 16: Model Identification Video
  2. Week 04: Lecture 17: Model Estimation Video
  3. Week 04: Lecture 18: Diagnostic Checking -1 Video
  4. Week 04: Lecture 19: Diagnostic Checking -2 Video
  5. Week 04: Lecture 20: Practical Session in R-4 Video

week-05

  1. Week 05: Lecture 21: Forecasting Basics Video
  2. Week 05: Lecture 22: Measuring Forecast Accuracy Video
  3. Week 05: Lecture 23: Smoothing Techniques (SMA,EMA) Video
  4. Week 05: Lecture 24: Double and Triple Exponential Smoothing Video
  5. Week 05: Lecture 25: Practical Session in R-5 Video

week-06

  1. Week 06: Lecture 26: Persistent and Long- Memory Processes : Examples and Implications Video
  2. Week 06: Lecture 27: ARFIMA Processes Video
  3. Week 06: Lecture 28: Hurst Exponent - Estimation under ARFIMA Video
  4. Week 06: Lecture 29: Estimation under ARFIMA Video
  5. Week 06: Lecture 30: Practical Session in R-6 Video

week-07

  1. Week 07: Lecture 31: Multivariate Time Series Analysis: Examples and Motivation Video
  2. Week 07: Lecture 32: Cross-covariance and Cross-correlation Video
  3. Week 07: Lecture 33: Some Specific Multivariate Time Series Models Video
  4. Week 07: Lecture 34: Further Extensions and Use Cases Video
  5. Week 07: Lecture 35: Practical Session in R - 7 Video

week-08

  1. Week 08: Lecture 36: Cointegration and Further Video
  2. Week 08: Lecture 37: Error Correction Models Video
  3. Week 08: Lecture 38: Tests for Cointegration Video
  4. Week 08: Lecture 39: Testing for Causality Video
  5. Week 08: Lecture 40: Practical Session in R - 8 Video

week-09

  1. Week 09: Lecture 41: Frequency Domain Analysis Video
  2. Week 09: Lecture 42: Spectral Representation of a Series Video
  3. Week 09: Lecture 43: Spectral Density Estimation Video
  4. Week 09: Lecture 44: Numerical Examples and Further Video
  5. Week 09: Lecture 45: Practical Session in R - 9 Video

week-10

  1. Week 10: Lecture 46: Stochastic Volatility Modelling Video
  2. Week 10: Lecture 47: ARCH Models Video
  3. Week 10: Lecture 48: ARCH LM Test and GARCH Models Video
  4. Week 10: Lecture 49: GARCH Model Extensions Video
  5. Week 10: Lecture 50: Practical Session in R - 10 Video

week-11

  1. Week 11: Lecture 51: Nonlinear Time Series Models Video
  2. Week 11: Lecture 52: Regimes and Nonlinear Models Video
  3. Week 11: Lecture 53: Nonlinear Model Extensions Video
  4. Week 11: Lecture 54: Markov Switching Models Video
  5. Week 11: Lecture 55: Practical Session in R - 11 Video

week-12

  1. Week 12: Lecture 56: Machine Learning in Time Series Video
  2. Week 12: Lecture 57: Linear Regression for Time Series and Beyond Video
  3. Week 12: Lecture 58: Other Machine Learning Models for Time Series Video
  4. Week 12: Lecture 59: Neural Networks for Time Series Video
  5. Week 12: Lecture 60: Practical Session in R - 12 Video

Live Session

  1. Live Session 27-02-2025 Video
  2. Live Session 26-03-2025 Video
  3. Live Session 07-04-2025 Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Bombay
Department
Computer Science and Engineering
Teacher
Prof. Sudeep Bapat
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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