Which statement best describes a time-series based forecasting method?

• A. It relies on expert opinion and surveys
• B. It uses causal relationships to predict demand
• C. It ignores historical data
• D. It analyzes historical data patterns to forecast future values

Answer: (D)

Time-series forecasting analyzes patterns in data collected over time to project future values. It relies on historical observations—such as trends, seasonal effects, and recurring patterns—to estimate what comes next, using methods like moving averages, exponential smoothing, or ARIMA. This data-driven approach contrasts with relying on expert opinions or surveys, which are qualitative, and with models that explain demand through external causal factors. It also wouldn't ignore historical data, since those past observations are the foundation for predicting future outcomes. So the statement that best describes a time-series method is that it analyzes historical data patterns to forecast future values.