Course

Module 1

Module 1: Introduction to utility of the R and Python (7.5 hours)

1. Introduction to the study & pedagogy.
2. Introduction to R and Python infrastructure for time series analysis.

Module 2

Module 2: Essential characteristics of the time-series data (7.5 hours)

1. Time Series and Their Features; Basic Descriptive Techniques.
2. Trends and Time Series Decomposition.

Module 3

Module 3: Forecasting Time Series and Smoothing (7.5 hours)

1. Smoothing Techniques
2. Introduction to Linear Time Series Models.
3. ARIMA Modeling.

Module 4

Module 4: Models for Time series and forecasting (7.5 hours)

1. Volatility Models.
2. State – Space Representation of the time series.
3. Time-Series forecasting and performance Evaluation.

Course Description

This course covers the methodology and applications of time series analysis and forecasting, focusing on issues and problems predicting business and economic data. The course is intended to serve as a guide to the principles, assumptions, strengths, limitations, and application of time series models and forecasting methods. This course introduces concepts essential to understanding the rationale of time series analysis. It reviews basic statistical principles and techniques that form the foundation for learning about time series methods. It covers most of the core techniques currently used in time series analysis. Data evaluation, identification of suitable models, estimation methods, and assessment of the models are explained with examples. Many examples of the application of time series analysis and forecasting to the problems in various business domains, including marketing, retail sales, human resource management, operations and supply chain management, finance, and general management.

Course Outcomes& Learning levels

This course provides a comprehensive background in time series analysis and forecasting. At the end of this course, the students.

1. I Will have acquired the skills to use time-series models in business analytics in an informed, disciplined way.
2. Will learn how to avoid common mistakes in the use of forecasting techniques and thereby move toward more sound, correct practices in all phases of the analysis.
3. Will be able to use time-series methods intelligently and get as much out of its application as possible
4. Will have enough knowledge to effectively demonstrate the application of time-series models using R and Python.

Evaluation Pattern

*Based on assignments / Tests / Quizzes / Case Studies / Projects / Term paper / Field visit report.

1. Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994) Time Series Analysis, Forecasting and Control, Englewood Cliffs, NJ: Prentice-Hall.
2. Chris Chatfield, and Haipeng Xing (2019) The Analysis of Time Series: An Introduction with R. CRC Press, London.
3. Galit Shmueli and Kenneth C. Lichtendahl Jr (2016). Practical Time Series Forecasting with R: A hands-on Guide, Axelrod Schnall Publishers.