A novel time series clustering method with fine-tuned support vector regression for customer behavior analysis

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).