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The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
A novel prediction model based on Gompertz function and customer life cycle (CLC) is presented in this paper. Firstly, the calling behavior between the inner-net (China Unicom) mobile customers and the outer-net (China Mobile) mobile customers are extracted and analyzed, then fitted a CLC curve by Gompertz function. Furthermore, the different period of CLC is identified according to the fitting curve...
Climate control for intelligent greenhouses is currently an active field of research. Model based intelligent greenhouse control systems seem to increase the control performance over traditional solutions. In this paper the control intelligence means the preferably minimal maintenance (heating) cost, within the climatic conditions required to grow sensitive floral cultures. To this purpose it is not...
Recent researches pay more attention to stock tendency prediction, which various machine learning approaches have been proposed. In this paper, we propose an algorithm to discover self-correlation of stock price in virtue of the notion of time series motifs, by viewing stock price sequences as time series. Generally, time series motif is a pattern appearing frequently in a time sequence, useful to...
Grey Model is with the characters of less date, high precision and without prior information. In the paper, a Grey Model GM (1, 1) and a Metabolizing Model are used for the fire prediction of some province and compared them to provide decision references for the concerning governments. The case shows that Grey Model is a simple process and effective practicality. GM (1, 1) is of higher precision and...
In this paper, multivariate time series models are built to predict the power ramp rate of a wind farm. The power changes are predicted at ten-minute intervals. Multivariate time series models are built with data-mining algorithms. Five different data-mining algorithms are tested using data collected at a wind farm. The support vector machine regression algorithm performed best of the five algorithms...
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