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

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...

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Vydané v:IEEE transactions on fuzzy systems Ročník 29; číslo 1; s. 4 - 18
Hlavní autori: Wen, Jiabao, Yang, Jiachen, Jiang, Bin, Song, Houbing, Wang, Huihui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Big Data
Clustering
Data mining
Data models
Datasets
Forecasting
forecasting model
fuzzy time series
Industrial development
long short-term memory (LSTM)
Marine environment
Mathematical models
Neural networks
Partitions (mathematics)
Prediction models
Predictive models
Redundancy
semisupervised learning
Sparks
Time series analysis
Training
ISSN:1063-6706, 1941-0034
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.3012393