Coming soon
TIME SERIES MODELS AND BUSINESS FORECASTING (Practical)
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 (COs):
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Learning outcome (at course level) |
Learning and teaching strategies |
Assessment Strategies |
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Course Code |
Course title |
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25MBB325 |
Time Series Models And Business Forecasting (Practical)
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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 |
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
ARIMA modelling: Autoregressive models, moving average models, smoothing Techniques, duality, model properties, parameter estimates, forecasts, Applications in Management
Volatility models: ARCH and GARCH modelling, testing strategy for heteroscedastic models, volatility forecasts, Forecasting errors, choosing the best method
Integrated processes: Difference stationarity, testing for unit roots, spurious correlation and Managing the forecasting process.
Multivariate time series: Time series regression, VAR models, cointegration, forecasting properties
• 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
E-RESOURCES RECOMMENDED:
• https://www.jigsawacademy.com/blogs/business-analytics/time-series-forec...
• https://nptel.ac.in/courses/103106123
• https://www.geeksforgeeks.org/
JOURNALS:
● https://vciba.springeropen.com/
● https://appliednetsci.springeropen.com/
