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.
Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.
Delves into extreme weather events, their definitions, causes, and consequences, emphasizing the importance of understanding climate change in relation to these occurrences.
Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.
