When historical data for forecasting is not available, which forecasting method is most appropriate?

When historical data for forecasting is not available, qualitative methods are the most appropriate approach. These methods rely on expert judgment, opinions, and intuition rather than on past data. They are particularly useful in situations where new products or markets are being considered, where previous experiences may not be applicable, or in rapidly changing environments where historical patterns may not hold.

Qualitative methods can include techniques such as market research, focus groups, and the Delphi approach, where panels of experts provide their insights that are then synthesized to produce forecasts. This approach allows for flexibility and creativity in situations where quantifiable data is lacking.

In contrast, time series methods depend on historical data to identify trends and patterns, quantitative methods often require numerical data to apply statistical techniques, and statistical methods also typically rely on established data sets. Therefore, these other approaches are not suitable when there is no prior data to inform the forecast.