Forecasting Basics

Most historical or time-based data contains an underlying trend or seasonal pattern. However, most historical data also contains random fluctuations (noise) that make it difficult to detect these trends and patterns without a computer. Predictive Planning uses sophisticated time-series methods to analyze the underlying structure of the data. It then projects the trends and patterns to predict future values.

Time-series forecasting breaks historical data into components: level, trend, seasonality, and error. Predictive Planning analyzes these components and then projects them into the future to predict likely results.

In Predictive Planning, a data series is a set of historical data for a single member. When you run a prediction, it tries each time-series method on each of the selected data series and calculates a mathematical measure of goodness-of-fit. Predictive Planning selects the method with the best goodness-of-fit as the method that will yield the most accurate forecast.

The final forecast shows the most likely continuation of the data. All of these methods assume that some aspects of the historical trend or pattern will continue into the future. However, the farther out you forecast, the greater the likelihood that events will diverge from past behavior, and the less confident you can be of the results. To help you gauge the reliability of the forecast, Predictive Planning provides a prediction interval indicating the degree of uncertainty surrounding the forecast.