Prediction method and method based on multi-source heterogeneous data

Abstract

The embodiment of the invention provides a prediction method and method based on multi-source heterogeneous data. The prediction method comprises the steps that at least two categories of historical data which can reflect an event result are obtained according to a set type of events; a joint likelihood model of attribute data of the set type of events and the historical data is built, and optimal estimation of the attribute data is determined according to the maximum posteriori principle; parameters of probability distribution related with the joint likelihood model in the attribute data are determined and used as a prediction result of a to-be-predicted event belonging to the set type of events. According to the prediction method and method based on the multi-source heterogeneous data, the hierarchical model is used, data from difference sources are introduced into different data layers, the heterogeneous data are unified in the same joint likelihood model to be analyzed, and therefore more accurate, real-time and stable prediction result can be obtained through effective fusion.

Description

technical field [0001] Embodiments of the present invention relate to data processing technologies, and in particular to a prediction method and device based on multi-source heterogeneous data. Background technique [0002] In the prior art, predicting event results based on historical data and models is a common prediction method. A typical application scenario is to predict the results of various competitions. [0003] The prediction model based on historical game data mainly estimates the team's offensive and defensive capabilities by analyzing the performance of each team in the historical game data, and uses this as a basis to predict the results of subsequent games. [0004] The main defect of this technical solution is that due to the relatively sparse time distribution of the games of each team, there are also changes in the players of the teams, the state fluctuations of the players themselves, and the contingency of the game, etc., so the prediction model obtained...

AI Technical Summary

Problems solved by technology

[0004] The main defect of this technical solution is that due to the relatively sparse time distribution of the games of each team, there are also changes in the players of the teams, the state fluctuations of the players themselves, and the contingency of the game, etc., so the prediction model obtained based on this is very difficult. It is difficult to make a good estimate of the real-time relative strength between all teams, resulting in poor prediction accuracy and instability

In addition, it is not possible to make a timely response to the situation in the schedule

Moreover, the data source is single, and the amount of information is small, so it cannot effectively predict the results of future competitions

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