Lecture 25 of 42week-06
Week 6: Lecture 14C: ARIMA: Auto-Regressive Moving Average (ARMA) Process
Prof. Pankaj Dutta · IIT Bombay
Credits and licence
- From
- Business Forecasting, IIT Bombay (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Week 1: Lecture 1: Introduction to Business Forecasting
- Week 1: Lecture 2A: Data Driven Decision Making and Essentials of Predictive Analytics
- Week 1: Lecture 2B: Data Driven Decision Making and Essentials of Predictive Analytics
- Week 1: Lecture 3: Types of Forecasting: Qualitative Approaches and Quantitative Approaches
- Week 2: Lecture 4A: Components of a Time Series and Measures of Forecast Accuracy
- Week 2: Lecture 4B: Components of a Time Series and Measures of Forecast Accuracy
- Week 2: Lecture 5A: Moving Average Methods: Simple, Weighted, and Exponential Moving Average
- Week 2: Lecture 5B: Moving Average Methods: Simple, Weighted, and Exponential Moving Average
- Week 3: Lecture 6: Exponential Smoothing
- Week 3: Lecture 7: Trend Projections and Holt Model
- Week 4: Lecture 8A: Simple Linear Regression and Measure of Goodness & Standard Error
- Week 4: Lecture 8B: Simple Linear Regression and Measure of Goodness & Standard Error
- Week 4: Lecture 8C: Simple Linear Regression and Measure of Goodness & Standard Error
- Week 4: Lecture 9A: Multiple Linear Regression and Multicollinearity
- Week 4: Lecture 9B: Multiple Linear Regression and Multicollinearity
- Week 4: Lecture 9C: Multiple Linear Regression and Multicollinearity
- Week 5: Lecture 10A: Seasonality, Seasonal Index, and Quarterly Average Method
- Week 5: Lecture 10B: Seasonality, Seasonal Index, and Quarterly Average Method
- Week 5: Lecture 11A: Seasonality and Trend: Winter's Holt Method
- Week 5: Lecture 11B: Seasonality and Trend: Winter's Holt Method
- Week 5: Lecture 12: Multiplicative Decomposition Method
- Week 6: Lecture 13: Autocorrelation Function (ACF) & Partial Autocorrelation Function (PACF)
- Week 6: Lecture 14A: ARIMA: Auto-Regressive (AR) Process
- Week 6: Lecture 14B: ARIMA: Moving Average (MA) Process
- Week 6: Lecture 14C: ARIMA: Auto-Regressive Moving Average (ARMA) Process
- Week 6: Lecture 14D: Auto-Regressive Integrated Moving Average (ARIMA) Model
- Week 7: Lecture 15A: Introduction to Machine Learning
- Week 7: Lecture 15B: Introduction to Machine Learning
- Week 7: Lecture 16A: Logistic Regression
- Week 7: Lecture 16B: Logistic Regression
- Week 7: Lecture 17: Human Judgment in Time Series Analysis
- Week 8: Lecture 18_A: Monte Carlo Simulation: Discrete Case
- Week 8: Lecture 18_B: Monte Carlo Simulation: Discrete Case
- Week 8: Lecture 18_C: Monte Carlo Simulation: Continuous case
- Week 8: Lecture 18_D: System Dynamics (Additional Learning)
- Week 8: Lecture 19_A: Predictive Analytics using @Risk Software
- Week 8: Lecture 19_B: Predictive Analytics using @Risk Software
- Week 8: Lecture 19_C: Predictive Analytics using @Risk Software
- Live Session 05-03-2024
- Live Session 08-04-2024
- Live Session 28-03-2025
- Live Session 23-04-2025