Time series methods are forecasting techniques that base the forecast solely on the demand history of the item you are forecasting. They work by capturing patterns in the historical data and extrapolating those patterns into the future. Time series methods are appropriate when you can assume a reasonable amount of continuity between the past and the future. They are best suited to shorter-term forecasting (say 18 months or less). This is due to their assumption that future patterns and trends will resemble current patterns and trends. This is a reasonable assumption in the short term but becomes more tenuous the further out you forecast.
Forecasting is a dynamic and interdisciplinary field that involves a multitude of people, processes and techniques. Forecasters are constantly building on their current pool of knowledge in order to improve their processes, drawing on whatever resources they can find. A forecaster’s best resource, however, is often other forecasters—but it can be difficult to share information if everyone is speaking a different language. For this post we’ve compiled and defined a list of terms that every forecaster should be familiar with. Continue reading