Time series adaptive mode decomposition (TAMD): Method for improving forecasting accuracy in the apparel industry

Accurate forecasting of apparel sales is critical for inventory management, supply chain optimization, and market strategy planning. However, existing forecasting models often struggle to effectively capture the complex characteristics of apparel sales data, such as distinct seasonality, cyclicality, and strongly nonlinear fluctuations, which significantly hinder prediction accuracy and generalization ability. To address these challenges, this study introduces a novel Time series Adaptive Mode Decomposition (TAMD)-based forecasting algorithm. The proposed method: (1) employs Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and sample entropy-guided Variational Mode Decomposition (VMD) to separate the input time series into noise components and multiple smooth Intrinsic Mode Functions (IMFs), to better capture intrinsic data dynamics; (2) refines the sub-series distribution features via an adaptive module guided by sample entropy, dividing each sub-series into subsequences with maximal distribution difference to improve adaptability to periodic changes and market volatility; (3) predicts each subsequence with adaptive distribution matching based on discontinuous random subsequence combinations, and then linearly superposes the prediction results as a final output, thereby boosting accuracy and generalizability. Comprehensive experiments on both public and self-constructed datasets (including four years of Taobao sales data for dresses, jeans, sweatshirts, and sweaters, totaling over 44.7 million records) demonstrate that TAMD outperforms existing methods significantly, highlighting its effectiveness in revealing the complexity of apparel market data and enhancing prediction performance.