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Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network

2020; Institute of Electrical and Electronics Engineers; Volume: 29; Issue: 1 Linguagem: Inglês

10.1109/tfuzz.2020.3012393

1941-0034

Jiabao Wen, Jiachen Yang, Bin Jiang, Houbing Song, Huihui Wang,

Energy Load and Power Forecasting

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.