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Abstract
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Exploring and forecasting customers’ behavior via time series analysis techniques has gained much attention in
recent years. In this context, distance-based time series clustering methods are widely utilized to divide customers into segments. However, the performance of distance-based clustering is highly influenced by the distance
metrics chosen. Determining a suitable distance metric for raw time series is a challenging task that must
consider several factors. Therefore, in this study, we focus on feature-based time series clustering and propose a
new featurization technique exploiting the Laplacian feature ranking method to obtain meaningful customer
segments. We evaluate the proposed featurization approach with four state-of-the-art clustering methods using
the point-of-sale (POS) transaction data. The clustering results indicate that the proposed method outperforms
the baseline method in terms of the Silhouette measure. Besides, we present an hybrid support vector regression
with the grasshopper optimization (SVRGOA) to forecast customers’ behavior. This method is compared against
three benchmarks, and the results reveal that SVRGOA outperforms other models in the majority of cases in
terms of the symmetric mean absolute percentage error (SMAPE).
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