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Statisticians

Quantitative Forecasting

by Scott Martin

Quantitative forecasting methods represent the relationship demand and one or more independent variables. Using quantitative forecasting is more objective than using qualitative forecasting.

Whenever you are going to conduct a quantitative forecast, you will need to collect the historical data which is relevant to your study in order to predict future conditions. This data should be checked for anomalies by plotting it and looking for outliers. If an anomaly is identified, it should be documented and removed from the dataset.

Quantitative forecasting can be categorized into two types of models. The first type, causal models, uses independent variables instead of (or as well as) time in order to generate a forecast. The second type, time series models, creates a demand profile with time as the independent variable.

Time Series Quantitative Forecasting

If your historical data shows a consistent demand that you expect to continue, using a time series model can be appropriate. This type of analysis attempts to describe trends via four components--trend, seasonal, cyclical, and random. For example, a seasonal component describes a trend that varies on a regular basis.

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