Objective of the course:
This is an introductory graduate level half semester course on forecasting and time series modeling. The focus of the course is to understand the applications of time-series model to actual business applications. The underlying statistical theory will be emphasized to the extent that it helps students set up an appropriate model.
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Syllabus:
Understanding and decomposition of time-series data, unit root and stationarity, moving average and exponential smoothing models, times-series regression models (AR, MA, ARIMA), dynamic causal regression, vector auto-regression (VAR) and volatility modeling (ARCH / GARCH models).
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Text Book:
Forecasting: principles and practice, RJ Hyndman, G Athanasopoulos 2018, https://otexts.com/fpp2 |
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Reference Material:
- The Analysis of Time-series: An Introduction with R by Chris Chatfield and Haipeng Xing
- Analysis of financial time series / Ruey S. Tsay. 3rd ed. (Wiley series in probability and statistics)
- Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics) by Galit Shmueli, Axelrod Schnall Publishers
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Evaluation scheme:
In order to benefit from this course, active participation is required from the students in classes and also out of classes doing assignments and project work.
| Attendance* |
- 10% |
| Quizzes (2) |
- 30% |
| Term Projects (1) |
- 30% |
| Final exam |
- 30% |
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| *(minimum of 65% attendance required - else deregistered from the course).
Attendance mandatory on the days of project presentation |
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| Academic Integrity: |
| If you are caught cheating or copying on any assignment, quiz or exam, you will |
- get ZERO marks on that assignment/quiz/exam.
- an additional penalty may be given including and upto assigning a grade F in the course.
| Week |
Lecture |
Topic |
| 1 |
1 |
Getting Started, Introduction to Time-series data |
| 1 |
2 |
Time-Series Graphics |
| 2 |
3 |
Time-series decomposition |
| 2 |
4 |
Forecaster’s toolbox |
| 3 |
5 |
Forecaster’s toolbox |
| 3 |
6 |
Time-series Regression Models |
| 4 |
7 |
Exponential smoothing |
| 4 |
8 |
Exponential smoothing |
| 5 |
9 |
ARIMA Models |
| 5 |
10 |
ARIMA Models |
| 6 |
11 |
Dynamic causal regression models |
| 6 |
12 |
Dynamic causal regression models |
| 7 |
13 |
Advanced Forecasting methods |
| 7 |
14 |
Advanced Forecasting methods |
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