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TRANSFORMING A TRANSACTIONAL DATA SET TO GENERATE FORECASTING AND PREDICTION INSIGHTS

机译:转换交易数据集以产生预测和预测见解

摘要

A method, system, and/or computer program product analyses event transactional related data to generate insights and predictions, which are pre-created to efficiently respond to requests for prediction/forecasting information, in order to improve the operation of the prediction-generating computer. One or more processors receive a series of structured data, where each entry (Ei) from the series of structured data has one or more time fields Tk and one or more attributes Aj. In response to determining that the series of structured data is transactional, one or more processors select a time field Tkr that meets an aggregation criterion, and then aggregate the transactional data from the time field Tkr into a time series data format. One or more processors consolidate results from a time series analysis and a regression analysis of the transformed transactional data to create a consolidated result, which is used to respond to a request for prediction/forecasting information.
机译:一种方法,系统和/或计算机程序产品,分析与事件交易相关的数据以生成见解和预测,这些见解和预测是预先创建的,以有效地响应对预测/预测信息的请求,从而改善预测生成计算机的操作。一个或多个处理器接收一系列结构化数据,其中一系列结构化数据中的每个条目(E i )具有一个或多个时间字段T k 和一个或多个属性A j 。响应于确定该一系列结构化数据是事务性的,一个或多个处理器选择满足聚合条件的时间字段T kr ,然后从时间字段T 聚合事务数据。 kr 转换为时间序列数据格式。一个或多个处理器将对转换后的交易数据进行时间序列分析和回归分析的结果合并,以创建合并结果,该合并结果用于响应对预测/预测信息的请求。

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