Continuous Multiple Prediction of Stream Data Based on Hierarchical Temporal Memory Network


KIPS Transactions on Computer and Communication Systems, Vol. 1, No. 1, pp. 11-20, Oct. 2012
10.3745/KTCCS.2012.1.1.11,   PDF Download:

Abstract

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.


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Cite this article
[IEEE Style]
C. Y. Han, S. J. Kim, H. S. Kang, "Continuous Multiple Prediction of Stream Data Based on Hierarchical Temporal Memory Network," KIPS Transactions on Computer and Communication Systems, vol. 1, no. 1, pp. 11-20, 2012. DOI: 10.3745/KTCCS.2012.1.1.11.

[ACM Style]
Chang Yeong Han, Sung Jin Kim, and Hyun Syug Kang. 2012. Continuous Multiple Prediction of Stream Data Based on Hierarchical Temporal Memory Network. KIPS Transactions on Computer and Communication Systems, 1, 1, (2012), 11-20. DOI: 10.3745/KTCCS.2012.1.1.11.