Time series forecasting (TSF) is fundamentally required in many real-world applications, such as electricity consumption planning and sales forecasting. In e-commerce, accurate time-series sales forecasting (TSSF) can significantly increase economic benefits. TSSF in e-commerce aims to predict future sales of millions of products. The trend and seasonality of products vary a lot, and the promotion activity heavily influences sales. Besides the above difficulties, we can know some future knowledge in advance except for the historical statistics. Such future knowledge may reflect the influence of the future promotion activity on current sales and help achieve better accuracy. However, most existing TSF methods only predict the future based on historical information. In this work, we make up for the omissions of future knowledge. Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales. Specifically, we design a knowledge-guided self-attention layer that uses known knowledge's consistency to guide the transmission of timing information. And the future-emphasized training strategy is proposed to make the model focus more on the utilization of future knowledge. Extensive experiments on four public benchmark datasets and one proposed large-scale industrial dataset from Tmall demonstrate that Aliformer can perform much better than state-of-the-art TSF methods. Aliformer has been deployed for goods selection on Tmall Industry Tablework, and the dataset will be released upon approval.
翻译:在电子商务中,准确的时间序列销售预测(TSF)可以大大增加经济效益。电子商务中的TSF旨在预测未来数百万产品的销售量。产品的趋势和季节性差异很大,促销活动对销售量影响很大。除了上述困难之外,我们可以事先知道一些未来知识,历史统计数据除外。这种未来知识可能反映未来促销活动对当前销售量的影响,并有助于提高准确性。但是,大多数现有的TSF方法只能根据历史信息预测未来。在这项工作中,我们弥补未来知识的遗漏。除了为预测提供未来知识外,我们建议基于双向变异器的Aliexer,它可以利用历史信息、当前因素和未来知识来预测未来销售量。具体地说,我们设计一个知识引导自留层,利用已知的知识一致性来指导时间信息传输。未来强调的培训战略是让模型更多地侧重于未来知识的利用未来知识。我们提出的四大类产业数据测试可以更好地展示最新数据选择。关于目前部署的四大类数据的实验将展示。