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