Abstract: According to the requirement of time series prediction accuracy and real-time and the trend and seasonal variation of time series in big data monitoring system, Holt-Winters algorithm is selected to build time series prediction model. First, this paper introduces the concept and characteristics of time series, then analyzes the principle of Holt-Winters algorithm and prediction conditions. Choosing the appropriate smoothing coefficient is the key to affect the accuracy of Holt-Winters algorithm. This paper introduces an algorithm for solving dynamic cubic smoothing coefficient in different time intervals by combining L-BFGS algorithm. Finally, the record of two-day page visits of users are used as experimental data. Through the analysis of relative error index, it is verified that the algorithm meets the requirements of big data monitoring system for time series prediction, and has better prediction application effect.

Key words: time series, Holt-Winters, big data monitoring system, smoothing coefficient