earticle

영어

Most time series big data is with noise and uncertain. To abstract the key information effectively and quickly, the estimation is one of the feasible methods for the uncertain big data. The Kalman filter with adaptive method by part of samples can give the high dimensional characteristics, reduce the computing cost and data uncertainty, but encounter the irregular estimation. The number of sample and the performance of the abstracted information have the tradeoff, which means we can use the suitable number of sample to abstract the key information of the series data. This paper discusses how to find the suitable sampling points for the time series data and the simulations show that the key dynamic information of time series big data can be guaranteed with the compression amount number of sample data.