Title
Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network
Abstract
AbstractThe 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.
Year
DOI
Venue
2021
10.1109/TFUZZ.2020.3012393
Periodicals
Keywords
DocType
Volume
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
Journal
29
Issue
ISSN
Citations 
1
1063-6706
5
PageRank 
References 
Authors
0.39
25
5
Name
Order
Citations
PageRank
Jiabao Wen1164.61
Jiachen Yang212016.19
Bin Jiang38513.70
Houbing Song41771172.26
Huihui Wang5202.66