NOC:Time Series Modelling and Forecasting with Applications in R

Lecture 1 - Time series introduction

Lecture 2 - Examples of time series data

Lecture 3 - Stationarity in time series

Lecture 4 - Weak vs.strong stationarity

Lecture 5 - Practical session in R-1

Lecture 6 - Time Series Decomposition

Lecture 7 - Basic Time Series Processes

Lecture 8 - Autocorrelation and the Partial Autocorrelation Functions

Lecture 9 - ACF and PACF for Some Time Series Processes

Lecture 10 - Practical Session in R-2

Lecture 11 - Non-Stationary Time Series

Lecture 12 - Seasonality and its Features

Lecture 13 - Cyclicality and Test for Stationarity

Lecture 14 - Seasonality and SARIMA Model

Lecture 15 - Practical Session in R-3

Lecture 16 - Model Identification

Lecture 17 - Model Estimation

Lecture 18 - Diagnostic Checking - 1

Lecture 19 - Diagnostic Checking - 2

Lecture 20 - Practical Session in R-4

Lecture 21 - Forecasting Basics

Lecture 22 - Measuring Forecast Accuracy

Lecture 23 - Smoothing Techniques (SMA,EMA)

Lecture 24 - Double and Triple Exponential Smoothing

Lecture 25 - Practical Session in R-5

Lecture 26 - Persistent and Long- Memory Processes : Examples and Implications

Lecture 27 - ARFIMA Processes

Lecture 28 - Hurst Exponent - Estimation under ARFIMA

Lecture 29 - Estimation under ARFIMA

Lecture 30 - Practical Session in R-6

Lecture 31 - Multivariate Time Series Analysis: Examples and Motivation

Lecture 32 - Cross-covariance and Cross-correlation

Lecture 33 - Some Specific Multivariate Time Series Models

Lecture 34 - Further Extensions and Use Cases

Lecture 35 - Practical Session in R-7

Lecture 36 - Cointegration and Further

Lecture 37 - Error Correction Models

Lecture 38 - Tests for Cointegration

Lecture 39 - Testing for Causality

Lecture 40 - Practical Session in R-8

Lecture 41 - Frequency Domain Analysis

Lecture 42 - Spectral Representation of a Series

Lecture 43 - Spectral Density Estimation

Lecture 44 - Numerical Examples and Further

Lecture 45 - Practical Session in R-9

Lecture 46 - Stochastic Volatility Modelling

Lecture 47 - ARCH Models

Lecture 48 - ARCH LM Test and GARCH Models

Lecture 49 - GARCH Model Extensions

Lecture 50 - Practical Session in R-10

Lecture 51 - Nonlinear Time Series Models

Lecture 52 - Regimes and Nonlinear Models

Lecture 53 - Nonlinear Model Extensions

Lecture 54 - Markov Switching Models

Lecture 55 - Practical Session in R-11

Lecture 56 - Machine Learning in Time Series

Lecture 57 - Linear Regression for Time Series and Beyond

Lecture 58 - Other Machine Learning Models for Time Series

Lecture 59 - Neural Networks for Time Series

Lecture 60 - Practical Session in R-12