Time Series Models and Business Forecasting

Paper Code: 
MBB 325
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course outcomes

Learning and

teaching strategies

Assessment

Strategies

On completion of this course, the students will be able to;

CO 1. Analyze time series based business data.

CO 2. Apply ARIMA modeling of stationary and non-stationary time series.

CO 3. Identify frequently used volatility models and inspect the problems arising when analyzing unit root processes.

CO 4. Identify and select testing strategy for volatility models.

CO 5. Apply analytics on real world time series and forecast results.

CO 6. Critically review and evaluate time series models and choose

the best modelling approach.

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

 

18.00
Unit I: 

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

 

18.00
Unit II: 

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

 

18.00
Unit III: 

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

 

18.00
Unit IV: 

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

 

18.00
Unit V: 

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

 

 

Essential Readings: 
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

 

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

 

References: 
E-RESOURCES RECOMMENDED:

 

JOURNALS:

 

Academic Year: 
2023-2024
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