Four 1 million-level apparel sales datasets（dresses, jeans, sweaters, and sweatshirts）

These datasets were developed through a collaboration between Zhejiang Sci-Tech University and Hangzhou Zhiyi Technology Co., Ltd., encompassing a four-year span from January 2019 to October 2023. We comprehensively document daily sales records of dresses, jeans, sweatshirts, and sweaters that maintained a sales volume exceeding 50 pieces and continued to sell for over 100 days. The dress dataset witnessed a maximum surge to 6.1 million, more than 12 times the smooth value at the surge point, while the jeans dataset recorded a peak of 7.3 million, over 18 times the smooth value. Similarly, the sweatshirt and sweater datasets exhibited surges of 13 and 16 times the smooth values, respectively. Such extreme volatility in the datasets poses a challenge for conventional algorithmic models to learn mutation data effectively without compromising linear fitting accuracy.

本数据集由浙江科技学院与杭州知意科技有限公司共同研发而成，历时四年，自2019年1月至2023年10月。数据集全面记录了连衣裙、牛仔裤、运动衫和毛衣等商品的日销售记录，这些商品的销售量超过50件，且连续销售超过100天。连衣裙数据集在峰值时达到610万，是平稳值点的12倍以上，而牛仔裤数据集则记录了730万的峰值，超过平稳值的18倍。类似地，运动衫和毛衣数据集的峰值分别达到平稳值的13倍和16倍。数据集中这种极端的波动性对传统算法模型提出了挑战，即在保证线性拟合精度不受影响的前提下，有效学习突变数据。