Compressing sampling for time series big data

Due to the noise and uncertainty, it is necessary for time series big data to capture the key information with estimation methods. 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 a tradeoff, which means we can use a 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 information of time series big data can be extracted effectively with the compression amount number of sample data.