What are the two main categories of quantitative forecasting?

What are the two main categories of quantitative forecasting?

• A. Time Series and Cause and Effect
• B. Naive forecasting and simple moving average
• C. Exponential smoothing and linear trend
• D. Customer survey and qualitative forecasting

Answer: (A)

Quantitative forecasting methods fall into two broad families: time series and causal (cause-and-effect) approaches. Time series uses historical data patterns over time—trends, seasonality, cycles, and random variation—to predict future values without explicitly tying the forecast to external factors. Causal forecasting, on the other hand, builds models that relate the forecast variable to one or more external drivers, such as price, advertising spend, or economic indicators, to explain and predict outcomes. These two categories capture the main ways we turn data into forecasts: one leverages patterns in the past, the other uses relationships with other influencing variables. The other options mix specific techniques that belong to these families or refer to non-quantitative methods, so they don’t represent the two main categories of quantitative forecasting.

Quantitative forecasting methods fall into two broad families: time series and causal (cause-and-effect) approaches. Time series uses historical data patterns over time—trends, seasonality, cycles, and random variation—to predict future values without explicitly tying the forecast to external factors. Causal forecasting, on the other hand, builds models that relate the forecast variable to one or more external drivers, such as price, advertising spend, or economic indicators, to explain and predict outcomes. These two categories capture the main ways we turn data into forecasts: one leverages patterns in the past, the other uses relationships with other influencing variables. The other options mix specific techniques that belong to these families or refer to non-quantitative methods, so they don’t represent the two main categories of quantitative forecasting.