Small Improvements Causing Substantial Savings - Forecasting Intermittent Demand Data Using SAS Forecast Server

Overview

Businesses require accurate forecasts of time series data that is not continuous. Often, time series data is intermittent (discontinuous or interrupted). Intermittent time series data points are mostly zero (the base value), with occasional departures from the base value. Intermittent time series are common in business and economic data. For example, at progressively lower levels of data disaggregation (larger frequency, smaller geography, or both), the time series data is often intermittent. The most commonly used forecasting techniques are continuous time series methods such as Exponential Smoothing Methods (ESM). Continuous methods are meant to forecast the future values with respect to future time periods.