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Time series forecasting (TSF) is fundamentally required in many real-worldapplications, such as electricity consumption planning and sales forecasting.In e-commerce, accurate time-series sales forecasting (TSSF) can significantlyincrease economic benefits. TSSF in e-commerce aims to predict future sales ofmillions of products. The trend and seasonality of products vary a lot, and thepromotion activity heavily influences sales. Besides the above difficulties, wecan know some future knowledge in advance except for the historical statistics.Such future knowledge may reflect the influence of the future promotionactivity on current sales and help achieve better accuracy. However, mostexisting TSF methods only predict the future based on historical information.In this work, we make up for the omissions of future knowledge. Except forintroducing future knowledge for prediction, we propose Aliformer based on thebidirectional Transformer, which can utilize the historical information,current factor, and future knowledge to predict future sales. Specifically, wedesign a knowledge-guided self-attention layer that uses known knowledge'sconsistency to guide the transmission of timing information. And thefuture-emphasized training strategy is proposed to make the model focus more onthe utilization of future knowledge. Extensive experiments on four publicbenchmark datasets and one proposed large-scale industrial dataset from Tmalldemonstrate that Aliformer can perform much better than state-of-the-art TSFmethods. Aliformer has been deployed for goods selection on Tmall IndustryTablework, and the dataset will be released upon approval.

From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba