orecasting is the use of historical data to determine trends in order to scale and shape resources. Reliable and accurate forecasting can contribute to meeting goals such as ensuring that supply and demand are met effectively. Supply and demand are hard to properly match and that’s because demand can fluctuate for a number of reasons while supply levels can only remain constant over a period of time.
Having said that…
There are three common types of forecasting; demand, supply, and price forecasts. In this case we’re trying to find a good approach to forecast fuel prices. Our text mentions two options:
Quantitative forecasting models focuses on historical data from time-series or correlation information.
Qualitative forecasting models can be based on opinions from experts, decision makers, or customers.
Now, the time series model is a good approach to forecast fuel prices but I would also consider other approaches that deal with variables other than time (e.g. casual model and qualitative models such as the Delphi method)
Can you think of ways that these other models can help us forecast fuel prices?