Learn · Application of Forecasting, Regression and Time Series Models
Lecture 17 of 60Week 4
lecture 17:Forecasting Example and Metrics used for analysing forecasting models
Prof. Raghu Nandan Sengupta · IIT Kanpur
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- Application of Forecasting, Regression and Time Series Models, IIT Kanpur (at NPTEL)
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All the lectures
- Lecture 1: Examples in Forecasting for Illustration
- Lecture 2: Quantitative & 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 of 2)
- Lecture 22: Holt winter method (Additive Seasonality)(Part-2 of 2)
- Lecture 23: Holt winter method (Multiplicative Seasonality)(Part-1 of 2)
- Lecture 24: Holt winter method (Multiplicative Seasonality)(Part-2 of 2)
- Lecture 25: Introduction of Regression
- Lecture 26: Introduction of Regression - Explanation with diagrams
- Lecture 27: Examples of Regression (Part 01 of 02)
- Lecture 28: Examples of Regression (Part 02 of 02)
- 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 Other Areas
- Lecture 38: Examples of MLR in Other 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, Expected Value of Loss, Risk of Estimation
- Lecture 42: Balanced loss function (with SEL and LINEX Combination)
- Lecture 43: Balanced loss function (with SEL and LINEX Loss Combinations), Other Loss functions
- Lecture 44: Logistic Regression
- Lecture 45: Non linear regression
- Lecture 46: Nonlinear regression, Examples (Rectangular Hyperbola Logarithmic)
- Lecture 47: Nonlinear Regression, Examples (Power, Negative Exponential, Asymptotic)
- Lecture 48: Nonlinear Regression, Examples (Exponential, Michaelis-Menten)
- Lecture 49: Nonlinear Regression, Examples (Logistic, Polynomial)
- Lecture 50: Probit Regression Models, Examples
- Lecture 51: Introduction, Examples
- Lecture 52: Auto Covariance Function and Auto Correlation Function
- Lecture 53:Auto Covariance Function and Auto Correlation, Function, Stationarity and Non stationarity
- Lecture 54: Constant Mean, Constant Variance, Constant Co-Variance, Autoregressive Integrated Moving Average, Examples
- Lecture 55: Auto Regressive Integrated Moving Average, Invertible, 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, Example of ARMA(p,q) Model
- 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, AGARCH, Ljung - Box - Pierce Q-Test