Abstract: For the online precise forecasting of time series，the hybrid forecasting algorithm based on multiple algorithms is established.The Judicature De-Noising algorithm（JDN） is first presented，which realizes de-noising & new steady-state disposal of time series data and reserves their original information.The Empirical Mode Decomposition（EMD） is then utilized to decompose the de-noised data.Next，the BP Neural Network（NN） and Least Square Support Vector Machines（LSSVM） are used to predict the low frequency items and high frequency items of the decomposed sequence data respectively，and the synthesis online realization of the proposed precise forecasting method for time series is finally provided.The algorithm can conquer its divergence by dealing with high frequency items using BP and its long computing time by predicting low frequency items using LSSVM.The simulation and practical experiment results，based on the breath period data of many patients，indicate that the algorithm realized online can forecast the time series precisely.Moreover，the forecasting error is less than that of single BP and the computing time is less than that of single LSSVM.