期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 29, 期 1, 页码 4-18出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.3012393
关键词
Time series analysis; Big Data; Predictive models; Data models; Forecasting; Sparks; Training; Big data; forecasting model; fuzzy time series; long short-term memory (LSTM); semisupervised learning
资金
- National Natural Science Foundation of China [61871283]
- Major Civil-Military Integration Project of Tianjin City [18ZXJMTG00170]
- Natural Science Foundation of Tianjin City [18JCJQJC46400]
This article proposes a new method using a semi-supervised prediction model and neural network model to solve the time analysis problem of massive industry data, and experimentally verifies its satisfactory predictive effect.
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.
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