Finding sequence patterns is a goal of data mining, which is concerned with finding statistically relevant patterns between example data, and the values are delivered in a sequence (Jea et al., 2009). Studies usually assume these values are discrete and, thus, time-series mining is closely related (Jea et al., 2009). Through sequential patterns, repeated events can be identified, even when no obvious relationships exist among events (Hu & Kao, 2011). However, not all events are worthy of study. Therefore, for different businesses, the importance of repeated events should be treated based on business requirements. For example, a repeated item may be ignored because of its low cost. In this study, the importance of factors identified in analytical results by the AHP was used as selection criteria for repeated foods. Through AHP analysis and using meaningful criteria for the sequential pattern of goods, the prediction of forthcoming input foods should be found.