NOC:Application of Forecasting, Regression and Time Series Models

Lecture 1 - Examples in Forecasting for Illustration

Lecture 2 - Quantitative and Qualitative Forecasting and Steps for Forecasting

Lecture 3 - Types of Forecasting and measures for Forecasting

Lecture 4 - Numerical Measures and Metrics of Forecasting

Lecture 5 - Examples of Forecasting; Auto Correlation Function and Correlogram

Lecture 6 - AIC, Ideas of Overfitting and Underfitting, Metrics of Analyzing

Lecture 7 - Example of Forecasting, Estimation of Interval Tolerance and Prediction

Lecture 8 - Distribution of Errors, Example of errors and MSE

Lecture 9 - Averaging Techniques

Lecture 10 - Averaging Techniques

Lecture 11 - Exponential Smoothing Forecasting Single Non-Adaptive Method

Lecture 12 - Exponential Smoothing Forecasting Multiple Non-Adaptive Parameters

Lecture 13 - Exponential Smoothing Single Adaptive Method - Part 1

Lecture 14 - Exponential Smoothing Single Adaptive Method - Part 2

Lecture 15 - Exponential Smoothing Single Adaptive Method - Part 3

Lecture 16 - Forecasting Example

Lecture 17 - Forecasting Example and Metrics used for analysing forecasting models

Lecture 18 - Exponential smoothing forecasting multiple adaptive method

Lecture 19 - Holt's Linear Method - Part 1

Lecture 20 - Holt's Linear Method - Part 2

Lecture 21 - Holt winter method (Additive Seasonality) - Part 1

Lecture 22 - Holt winter method (Additive Seasonality) - Part 2

Lecture 23 - Holt winter method (Multiplicative Seasonality) - Part 1

Lecture 24 - Holt winter method (Multiplicative Seasonality) - Part 2

Lecture 25 - Introduction of Regression

Lecture 26 - Introduction of Regression - Explanation with diagrams

Lecture 27 - Examples of Regression - Part 1

Lecture 28 - Examples of Regression - Part 2

Lecture 29 - Examples of Regression - Multiple Linear Regression

Lecture 30 - Multiple Linear Regression

Lecture 31 - Multiple Linear Regression, homoscedasticity, heteroscedasticity, non-autocorrelation, autocorrelation

Lecture 32 - Multiple Linear Regression, OLS estimate of β, Projection, Mean of estimate of β, Variance of estimate of β

Lecture 33 - Multiple Linear Regression, Unbiased Estimate of σ2 , CAPM, Market Line

Lecture 34 - Discussion about SLR in Finance

Lecture 35 - Examples of SLR and MLR in Finance

Lecture 36 - Examples of MLR Finance

Lecture 37 - Examples of MLR Finance and Others Areas

Lecture 38 - Examples of MLR Finance and Others Areas

Lecture 39 - LINEX Loss Function, Estimation Under LINEX Loss Function

Lecture 40 - LINEX Loss Function, Expected Value of Loss, Risk of Estimation

Lecture 41 - LINEX loss function

Lecture 42 - Balanced loss function

Lecture 43 - Other loss function

Lecture 44 - Logistic Regression

Lecture 45 - Non linear regression

Lecture 46 - Multiple linear regression

Lecture 47 - Non linear Regression

Lecture 48 - Exponential Regression model

Lecture 49 - Logistic Response model

Lecture 50 - Probit Regression Models

Lecture 51 - Introduction to Time Series Models

Lecture 52 - Auto Covariance and correlation function

Lecture 53 - Stationarity and Non stationarity

Lecture 54 - Autoregressive Integrated Moving Average

Lecture 55 - Invertible and non invertible

Lecture 56 - Auto Regressive (AR) Process, Stationarity, Causality

Lecture 57 - ARMA(p,q), Autocovariance Function, Autocorrelation Function, Partial Autocorrelation Function

Lecture 58 - Stationary, Non stationary, Mean squared Error, Preliminy Estimation, Maximum Likelihood Estimation

Lecture 59 - GARCH Volatility Clustering, Box- Pierce LM, AGARCH Conditional Heteroscedasticity, Conditional Variance

Lecture 60 - ARCH (p), GARCH (p,q) and why it is Related to ARCH, EGARCH, Ljung - Box - Pierce Q-Test