The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This article proposes a semisupervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this article is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.

中文翻译：

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大数据驱动的海洋环境信息预测：时间序列预测网络

行业大数据技术的不断发展需要更好的计算方法来发现数据价值。信息预测作为数据挖掘技术的重要组成部分，在一些行业取得了很好的应用。然而，传感器收集的数据存在偏差和冗余，使得某些方法难以准确预测未来信息。本文提出了一种半监督预测模型，利用改进的无监督聚类算法建立模糊划分函数，然后利用神经网络模型构建信息预测函数。本文主要目的是为了有效解决海量行业数据的时间分析问题。在实验部分，我们在Spark上搭建了一个数据平台，并使用一些海洋环境因子数据集和UCI公共数据集作为分析对象。同时，我们分析了该方法与其他传统方法的比较结果，以及在Spark平台上的运行性能。结果表明，所提出的方法取得了满意的预测效果。