Explores the significance of weather forecasting, ensemble forecasts like Hurricane Katrina, and the operational use of hydrological forecasting systems.
Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.
Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.
Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.
Delves into extreme weather events, their definitions, causes, and consequences, emphasizing the importance of understanding climate change in relation to these occurrences.
