Which forecasting approach is described as the most accurate?

• A. Judgemental Approach
• B. Regression Technique
• C. Time Series Approach
• D. Relational Approach

Answer: (C)

Forecast accuracy improves when predictions are grounded in patterns actually observed over time. A time series approach does precisely that by analyzing sequences of past observations to identify components such as trend, seasonality, cycles, and randomness, and then using those components to project future values. Because it bases forecasts on real data and updates as new observations come in, it naturally adapts to changing patterns and often yields more accurate results than approaches that rely on subjective judgment.

Judgmental forecasting depends on human judgment, which can introduce bias and inconsistency. Regression techniques require stable relationships with predictors, which may not hold if drivers change or data are noisy. Relational approaches use external factors, but if those relationships aren’t well specified or data are unavailable, accuracy suffers. In many contexts, the time series method therefore provides the strongest, data-driven accuracy among these options.