Learn · Time Series Modelling and Forecasting with Applications in R
Lecture 29 of 63week-06
Week 06: Lecture 29: Estimation under ARFIMA
Prof. Sudeep Bapat · IIT Bombay
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- Time Series Modelling and Forecasting with Applications in R, IIT Bombay (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Week 01: Lecture 01: Time series introduction
- Week 01: Lecture 02: Examples of time series data
- Week 01: Lecture 03: Stationarity in time series
- Week 01: Lecture 04: Weak vs. strong stationarity
- Week 01: Lecture 05: Practical session in R - 1
- Week 02: Lecture 06: Time Series Decomposition
- Week 02: Lecture 07: Basic Time Series Processes
- Week 02: Lecture 08: Autocorrelation and the Partial Autocorrelation Functions
- Week 02: Lecture 09: ACF and PACF for Some Time Series Processes
- Week 02: Lecture 10: Practical Session in R-2
- Week 03: Lecture 11: Non-Stationary Time Series
- Week 03: Lecture 12: Seasonality and its Features
- Week 03: Lecture 13: Cyclicality and Test for Stationarity
- Week 03: Lecture 14: Seasonality and SARIMA Model
- Week 03: Lecture 15: Practical Session in R-3
- Week 04: Lecture 16: Model Identification
- Week 04: Lecture 17: Model Estimation
- Week 04: Lecture 18: Diagnostic Checking -1
- Week 04: Lecture 19: Diagnostic Checking -2
- Week 04: Lecture 20: Practical Session in R-4
- Week 05: Lecture 21: Forecasting Basics
- Week 05: Lecture 22: Measuring Forecast Accuracy
- Week 05: Lecture 23: Smoothing Techniques (SMA,EMA)
- Week 05: Lecture 24: Double and Triple Exponential Smoothing
- Week 05: Lecture 25: Practical Session in R-5
- Week 06: Lecture 26: Persistent and Long- Memory Processes : Examples and Implications
- Week 06: Lecture 27: ARFIMA Processes
- Week 06: Lecture 28: Hurst Exponent - Estimation under ARFIMA
- Week 06: Lecture 29: Estimation under ARFIMA
- Week 06: Lecture 30: Practical Session in R-6
- Week 07: Lecture 31: Multivariate Time Series Analysis: Examples and Motivation
- Week 07: Lecture 32: Cross-covariance and Cross-correlation
- Week 07: Lecture 33: Some Specific Multivariate Time Series Models
- Week 07: Lecture 34: Further Extensions and Use Cases
- Week 07: Lecture 35: Practical Session in R - 7
- Week 08: Lecture 36: Cointegration and Further
- Week 08: Lecture 37: Error Correction Models
- Week 08: Lecture 38: Tests for Cointegration
- Week 08: Lecture 39: Testing for Causality
- Week 08: Lecture 40: Practical Session in R - 8
- Week 09: Lecture 41: Frequency Domain Analysis
- Week 09: Lecture 42: Spectral Representation of a Series
- Week 09: Lecture 43: Spectral Density Estimation
- Week 09: Lecture 44: Numerical Examples and Further
- Week 09: Lecture 45: Practical Session in R - 9
- Week 10: Lecture 46: Stochastic Volatility Modelling
- Week 10: Lecture 47: ARCH Models
- Week 10: Lecture 48: ARCH LM Test and GARCH Models
- Week 10: Lecture 49: GARCH Model Extensions
- Week 10: Lecture 50: Practical Session in R - 10
- Week 11: Lecture 51: Nonlinear Time Series Models
- Week 11: Lecture 52: Regimes and Nonlinear Models
- Week 11: Lecture 53: Nonlinear Model Extensions
- Week 11: Lecture 54: Markov Switching Models
- Week 11: Lecture 55: Practical Session in R - 11
- Week 12: Lecture 56: Machine Learning in Time Series
- Week 12: Lecture 57: Linear Regression for Time Series and Beyond
- Week 12: Lecture 58: Other Machine Learning Models for Time Series
- Week 12: Lecture 59: Neural Networks for Time Series
- Week 12: Lecture 60: Practical Session in R - 12
- Live Session 27-02-2025
- Live Session 26-03-2025
- Live Session 07-04-2025