Air Cargo Exam 2 Practice

Time series models are built specifically to forecast future values from data that are indexed in time, using patterns like trend, seasonality, and autocorrelation to make predictions about upcoming periods. They estimate parameters from past observations and then project those patterns forward, often providing uncertainty intervals for the forecasts. Other methods shown can be used for prediction, but they don’t inherently embody the time-ordered forecasting framework. Decision trees predict outcomes by splitting on features and don’t automatically account for temporal structure unless you create time-based features. Monte Carlo methods simulate many possible outcomes to explore risk and variability rather than producing a direct time-forward forecast. Neural networks can forecast when structured for sequential data (like LSTMs), but time series modeling remains the standard category specifically designed for forecasting future observations from time-ordered data.