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PROACTIVELY PREDICTING TRANSACTION DATES BASED ON SPARSE TRANSACTION DATA
PROACTIVELY PREDICTING TRANSACTION DATES BASED ON SPARSE TRANSACTION DATA
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机译:主动预测基于稀疏事务数据的交易日期
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摘要
The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.
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