Reference no: EM133876290

Question

Evaluating errors in a forecasting model is an important step to understand if it provides accurate predictions. Typically, the following forecasting models are used: mean absolute error, mean squared error, root mean squared error, mean absolute percentage error, r-squared, and forecast bias. A good model provides future values with minimal errors or close to the actual values. The model is consistent, low bias, is generalizable, and fits in a business context. To ensure the model is accurate, it is important to use cross-validation, different forecasting techniques, and relevant features, regularly update the models based on patterns or relationships, and incorporate domain-specific knowledge into the model. An example of a bad forecasting model would be a hospital administrator underestimating the influx of patients, causing overcrowding of hospital beds and overuse of resources (ie, what happened at many hospitals during the pandemic). If the model underestimates the demand of patients, the hospital will not be prepared to handle the surge of patients. This can lead to long wait times, poor patient care, and potential health risks for the patients and healthcare providers.