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Home > TIME SERIES MODELS AND BUSINESS FORECASTING (Practical)

TIME SERIES MODELS AND BUSINESS FORECASTING (Practical) [1]

Paper Code: 
25MBB325
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

The course will enable students to understand and apply time series analysis techniques, including ARIMA and volatility models, to forecast and manage data in various contexts, with an emphasis on multivariate time series and appropriate forecasting methods.

Course Outcomes: 

Course Outcomes (COs):

Course

Learning outcome

(at course level)

Learning and teaching

strategies

Assessment Strategies

Course Code

Course title

25MBB325

Time Series Models And Business Forecasting

(Practical)

 

CO349: Analyze time    series                          based

business data.

CO350: Apply ARIMA modeling of stationary and non-stationary time series.

CO351: Identify frequently used volatility models and inspect the problems arising when analyzing unit root processes.

CO352: Identify and select testing strategy for volatility models.

CO353: Apply analytics on real world time series and forecast results.

CO354: Contribute effectively in course-specific interaction

Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activities for the students: Self-learning assignments, presentations

Class test, Semester endexaminations, Quiz, Assignments, Presentation

 

12.00
Unit I: 
Basic concepts in time series analysis

Basic concepts in time series analysis: stationarity, autocovariance, autocorrelation, partial autocorrelation, Exploring Time series data patterns, Types of forecasting Techniques and choosing the appropriate method of forecasting

12.00
Unit II: 
ARIMA modelling

ARIMA modelling: Autoregressive models, moving average models, smoothing Techniques, duality, model properties, parameter estimates, forecasts, Applications in Management

12.00
Unit III: 
Volatility models

Volatility models: ARCH and GARCH modelling, testing strategy for heteroscedastic models, volatility forecasts, Forecasting errors, choosing the best method

12.00
Unit IV: 
Integrated processes

Integrated processes: Difference stationarity, testing for unit roots, spurious correlation and Managing the forecasting process.

12.00
Unit V: 
Multivariate time series

Multivariate time series: Time series regression, VAR models, cointegration, forecasting properties

Essential Readings: 

• Mark J. Bennett, Dirk L. Hugen, Financial Analytics with R, Cambridge University Press
• John E Hanke, Dean W. Wichern, Business Forecasting, PHI Publications

References: 

Suggested readings
• George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung, (2017) Time Series Analysis: Forecasting and Control, Wiley, 5th Edition
• Aileen Nielsen (2019), Practical Time Series Analysis: Prediction with Statistics and Machine Learning, O'Reilly

E-RESOURCES RECOMMENDED:
• https://www.jigsawacademy.com/blogs/business-analytics/time-series-forec... [2]
• https://nptel.ac.in/courses/103106123 [3]
• https://www.geeksforgeeks.org/ [4]
JOURNALS:
● https://vciba.springeropen.com/ [5]
● https://appliednetsci.springeropen.com/ [6]

Academic Year: 
2025-2026 [7]

Follow Management Studies(B) on:

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Source URL: https://managementb.iisuniv.ac.in/courses/subjects/time-series-models-and-business-forecasting-practical

Links:
[1] https://managementb.iisuniv.ac.in/courses/subjects/time-series-models-and-business-forecasting-practical
[2] https://www.jigsawacademy.com/blogs/business-analytics/time-series-forecasting/
[3] https://nptel.ac.in/courses/103106123
[4] https://www.geeksforgeeks.org/
[5] https://vciba.springeropen.com/
[6] https://appliednetsci.springeropen.com/
[7] https://managementb.iisuniv.ac.in/academic-year/2025-2026