Course11010474360 lectures
Application of Forecasting, Regression and Time Series Models
Prof. Raghu Nandan Sengupta · IIT Kanpur
About
In today's world, when data is the ruler, banks, retailers, factories,hospitals, IT companies, etc., all use predictive analytics to make judgments and stay ahead. The mathematical concepts of Forecasting, Regression, and Time Series models assist in generating precise predictions, thereby equipping decision-makers to formulate rational, mathematical, and evidence-based solutions to diverse forecasting, regression, and time series problems. This course is both academic and practical, giving learners much hands-on experience. During interactive sessions, participants will learn to use several forecasting methods, linear and multivariate regression modelling, and time series analysis. The course will focus on how to read data, check the validity of models, and turn analytical insights into business strategies that can be used.
Intended audience: 4th Year UG, MTech, MBA, PhD
Prerequisites: BE/BSc Engg/BTech/BSc/BS-MS
Lectures
Week 1
- 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
Week 2
- 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
Week 3
- 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
Week 4
- 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)
Week 5
- 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
Week 6
- 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
Week 7
- 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
Week 8
- 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
Week 9
- 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
Week 10
- 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
Week 11
- 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
Week 12
- 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
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kanpur
- Department
- Management
- Teacher
- Prof. Raghu Nandan Sengupta
- Level
- Undergraduate
- Language
- English
- Subjects
- Business and management
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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