Abstract
This paper studies the problem of finding similar time series of product sales in transactional data. We argue that finding such similar time series can lead to discovery of interesting and actionable business information such as previously unknown complementary products or substitutes, and hidden supply chain information. However, finding all possible pairs of n time series exhaustively results in O(n 2) time complexity. To address this issue, we propose using k-means clustering method to create small clusters of similar time series, and those clusters with very small intra-cluster variability are used to find similar time series. Finally, we demonstrate the utility of our approach to derive interesting results from real-life data.
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Tan, S.C., Lau, P.S., Yu, X. (2015). Finding Similar Time Series in Sales Transaction Data. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_62
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DOI: https://doi.org/10.1007/978-3-319-19066-2_62
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