Lon-Mu Liu*, Siddhartha Bhatacharyya*, Stanley
L. Sclove*, Rong Chen*, Wm. J. Lattyak**
* Department of Information and Decision Sciences, University of Illinois at Chicago
** Scientific Computing Associates, Chicago, IL
Accepted for publication in Computational Statistics and Data Analysis.
Given the widespread use of modern information technology, a large number of time series may be collected during normal business operations. We use a fast-food restaurant franchise as a case to illustrate how data mining can be applied to such time series, and help the franchise reap the benefits of such an effort. Time series data mining at both the store level and corporate level are discussed. Related data warehousing issues are also addressed. Box-Jenkins seasonal ARIMA models are employed to analyze and forecast the time series. Instead of a traditional manual approach of Box-Jenkins modeling, an automatic time series modeling procedure is employed to analyze a large number of highly periodic time series. In addition, an automatic outlier detection and adjustment procedure is used for both model estimation and forecasting. The improvement in forecast performance due to outlier adjustment is demonstrated. Outlier detection also leads to information that can be used not only for better inventory management and planning but also to identify potential sales opportunities. Adjustment of forecasts based on stored historical estimates of like-events is also discussed. To illustrate the feasibility and simplicity of the above automatic procedures for time series data mining, the SCA Statistical System is employed to perform the related analysis.
Keywords: Automatic time series modeling, Expert system, Outliers, Knowledge discovery, Forecasting