Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales

The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation win...

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Veröffentlicht in:Journal of computational and applied mathematics 2020-05, Vol.370, p.112659, Article 112659
Hauptverfasser: Li, Hailin, Wu, Yenchun Jim, Chen, Yewang
Format: Artikel
Sprache:eng
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Zusammenfassung:The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation windows for a time series database that is transformed from a commodities transaction database. There are sales correlations in two time series at different times, and this may produce valuable rules and knowledge for those who wish to practice cross-marketing and earn greater profits. It means that observation time points denoting the time at which the sales correlation occurs constitute important information. The dynamic model that leverages the techniques inherent in time series data-mining can uncover the kinds of commodities that have similar sales trends and how those sales trends change within a particular time period, which indicates that the “right” commodities can be commended to the “right” customers at the “right” time. Moreover, some of the time periods used to pinpoint similar sales patterns can be used to retrieve much more valuable information, which can in turn be used to increase the sales of the correlated commodities and improve market share and profits. Analysis results of retail commodities datasets indicate that the proposed method takes into consideration the time factor, and can uncover interesting sales patterns by which to improve cross-marketing quality. Moreover, the algorithm can be regarded as an intelligent component of the recommendation and marketing systems so that human–computer interaction system can make intelligent decision. •Right commodities can be recommended to right customers at right time by the proposed method.•Dynamic model reflects the relationships of the goods at the different observation time points.•The beginning time and duration of correlation among goods can improve the sales quantity.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2019.112659