关键词: 油耗估计, 聚类分析, 朴素贝叶斯, 动态时间规整, 数据缺失

Abstract: Aiming at the problem of single discontinuous missing data and continuous missing data in the carbon emission report, the estimation error of using a single method is large, a combined estimation method based on cluster analysis is proposed. The method firstly uses the K-medoids clustering algorithm to classify the data into single discontinuous missing data and continuous missing data, and then uses the Naive Bayes （NB） method to estimate the single discontinuous data, uses Dynamic Time Warping （DTW） method to estimate the continuous missing data, and finally evaluates the estimation results at 1%, 2%, and 3% root mean square error. The simulation results show that the NB-DTW combination method based on cluster analysis can effectively reduce the estimation error, which is 9.3%, 12.1% and 12.96% lower than the NB method at 1%, 2% and 3% root mean square error, respectively, and reduced by 35.46%, 43.62% and 55.04% respectively than DTW method.

Key words: fuel consumption, cluster analysis, Naive Bayes, dynamic time warping;missing data