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Time Series Models and Business Forecasting
Course Outcomes (COs): Course Course outcome (at course level) Learning and teaching strategies Assessment Strategies Paper Code Paper Title CLO 180.Analyze time series based business data. CLO 181.Apply ARIMA modeling of stationary and non-stationary time series. CLO 182.Identify frequently used volatility models and inspect the problems arising when analyzing unit root processes. CLO 183.Identify and select testing strategy for volatility models. CLO 184.Apply analytics on real world time series and forecast results. CLO 185.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 end examinations, Quiz, Assignments, Presentation MBB 426 Time Series Models and Business Forecasting
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 Technques, 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 methbod
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
