초록

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Abstract ▼ AI-Helper

There is a wide range of applications such as social network services, sensor networks and data centers which generate time series data. Thus, analysis of such time series data has attracted a lot of attention in the recent years. In this paper, we propose a model called K-mRBF which utilizes a modi...

There is a wide range of applications such as social network services, sensor networks and data centers which generate time series data. Thus, analysis of such time series data has attracted a lot of attention in the recent years. In this paper, we propose a model called K-mRBF which utilizes a modified K-means clustering algorithm with the multivariate radial basis functions (mRBF) to predict future values based on previously observed values. We conduct extensive experiments using synthetic as well as real-life data sets to compare our K-mRBF model to the state-of-the-art model. Experimental results confirm the accuracy of our model compared to state-of-the-art models.

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