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Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single...
Wind power short-term prediction method generally depends on the meteorological data at present. This paper proposed time series power prediction method which is based on multi-scale tuple matching and can predict wind power well by making full use of historical data without affecting the computational efficiency to predict wind power on the occasion where power series can be obtained but the meteorological...
This paper is aimed at saving energy in information collection in wireless sensor networks. Our method is to keep sensor nodes from transmitting redundant information, which can be predicted by the sink node. By utilizing the ARIMA model for prediction, we propose an energy efficient information collection scheme. Since the ARIMA model is capable of modeling a wide variety of complex time series by...
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