A Real-Time Integrated Hierarchical Temporal Memory Network for the Real-Time Continuous Multi-Interval Prediction of Data Streams
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Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream basedon 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 thenumber of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchicaltemporal memory (RIHTM) network by introducing a new type of node, which is called aZeta1FirstSpecializedQueueNode (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 simplestructure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated inthe ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network duringthe prediction stage to generate different intervals of prediction results. The RIHTM network efficientlyreduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuouslypredict the trend of data streams with multi-intervals in the real-time mode.
Abstract
Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream basedon 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 thenumber of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchicaltemporal memory (RIHTM) network by introducing a new type of node, which is called aZeta1FirstSpecializedQueueNode (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 simplestructure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated inthe ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network duringthe prediction stage to generate different intervals of prediction results. The RIHTM network efficientlyreduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuouslypredict the trend of data streams with multi-intervals in the real-time mode.
참고문헌 (9)
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