Course

PROFESSIONAL ELECTIVES

Electives Electives in Data Science

Unit I

Planning and Forecasting – Forecasting process – Time Series patterns – Statistical fundamentals for forecasting –Descriptive statistics – Measuring errors – Correlation and Covariance – Autocorrelations – Linear Regression

analysis – Dependent and independent variables – Method of least square deviations – Durbin-Watson Statistic –Univariate methods

Unit II

Univariate ARIMA methods – ARIMA model identification – Time series examples – Integrated Stochastic process– Backward shift operator – Autoregressive processes – Yule-Walker equations – ARIMA prediction intervals -Multiple Regression models – Serial correlation – Elasticities and Logarithmic relationships – Heteroscedasticity –Intervention functions – Nonstationary series.

Unit III

Smoothing methods – Decomposition methods – Trend-Seasonal and Holt-Winters smoothing – SARIMA processes – SARIMA fitting – Akaike Information Criterion- Schwarz Bayesian Information Criterion and Model Quality.

Course Objectives

• To introduce the principles and methods of forecasting.
• To introduce various components of time series and time series models which cater to the real-world
• To help students explore and use the various criteria used for performance evaluation.
• To address both the aspects of descriptive and predictive analytics.

Course Outcomes

CO1: Understand the principles and process of forecasting.

CO2: Apply and analyze Univariate ARIMA methods for real world problems.

CO3: Apply and analyze Smoothing methods for real world problems.

CO4: Apply various criteria for evaluating model quality.

CO5: Apply and analyze multivariate methods for real world problems.

CO-PO Mapping

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports

*CAL – Can be Lab Assessments, Project, and Report

**End Semester can be theory examination/ lab-based examination/ project presentation

Textbook(s)

Stephen A. DeLurgio, “Forecasting Principles and Applications”, McGraw Hill International Editions 2 Revised ed Edition – 1 July 1998. ISBN-13: 978-0071159982 ISBN-10: 0071159983.

Galit Shmueli, Kenneth C. Lichtendahl Jr., Axelrod.,” Practical Time Series Forecasting with R: A Hands-On Guide”, Second Edition, Schnall Publishers; 2016

Rob J Hyndman and George Athanasopoulos, “Forecasting: Principles and Practice”, Third Edition, Otexts; 2018.

Reference(s)

Ruet S. Tsay, “Analysis of Financial Time Series”, 3^rd Edition, Wiley, New Jersy, 2015. 

Walter Enders, “Applied Econometrics”, 3^rd Edition, Wiley, New Jersy, 2014. 

Terence C. Mills, “The Foundations of Modern Time Series Analysis”, Palgrave Macmillan; 2011.

Kerry Patterson, “An Introduction to Applied Econometrics – A Time Series Approach”, Macmillan Press Limited;2000.