This week’s topics
Predictive Analytics for Trend and Demand Forecasting
- Time series decomposition
- ARIMA and exponential smoothing
- Linear and multivariate regression in programming environments
- Mod-02 Lec-03 Forecasting -- Linear Models, Regression, Holt's , seasonality
- Week 07: Lecture 34: Demand Forecasting using AI/ML: Building Regression Tree Model using Python
- Mod-02 Lec-02 Forecasting -- Time series models -- Simple Exponential smoothing
What you will be able to do
- 3.3 Apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events.
Activity
Start with this week’s 3 videos, then: Seminar, guided reading and a short written task: apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events.
Study materials
Videos
- Exponential Smoothing · YouTube · MIT Center for Transportation & Logistics · 15 min · for registered students
- What is Time Series Analysis? · YouTube · IBM Technology · 7 min · for registered students
- Forecasting Metrics · YouTube · MIT Center for Transportation & Logistics · 8 min · for registered students
- Linear Regression in R, Step by Step · YouTube · StatQuest with Josh Starmer · 5 min · for registered students
- Multiple Regression, Clearly Explained!!! · YouTube · StatQuest with Josh Starmer · 5 min · for registered students
Further resources
- Principles of Data Science, ch. 5: Time Series and Forecasting · PDF · OpenStax, textbook chapter; pp. 223–274 · for registered students
- MIT OCW ESD.260J Logistics Systems (Fall 2006), Demand forecasting I: time series · PDF · MIT OpenCourseWare, slides · for registered students
Materials marked “for registered students” open in the student portal, which is set up as soon as you receive final acceptance.
