JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Real-Time Integrated Hierarchical Temporal Memory Network for the Real-Time Continuous Multi-Interval Prediction of Data Streams |
| 영문제목(English Title) |
A Real-Time Integrated Hierarchical Temporal Memory Network for the Real-Time Continuous Multi-Interval Prediction of Data Streams |
| 저자(Author) |
Hyun-Syug Kang
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| 원문수록처(Citation) |
VOL 11 NO. 01 PP. 0039 ~ 0056 (2015. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode. |
| 키워드(Keyword) |
Data Streams
Hierarchical Temporal Memory
Multiple Interval Prediction
Real-Time Prediction
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| 파일첨부 |
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