CAJ | 학술논문

根据基于时间序列的船舶航行定位数据的特征，在差分自回归移动平均模型的基础上，运用马尔可夫链状态转移概率特性解决非平稳数据的预测问题，在建立马尔可夫链状态迁移概率矩阵过程中，使用K-means聚类算法划分预测值与真实值的差值状态区间，继而构建出优化预测算法。对算法进行了理论分析和数值实验，并与其他算法进行了比较，结果表明，该优化算法在船舶定位数据短时预测领域具有较好的预测效果，优于多个其他算法，可应用于船舶移动定位产品中。
근거기우시간서렬적선박항행정위수거적특정，재차분자회귀이동평균모형적기출상，운용마이가부련상태전이개솔특성해결비평은수거적예측문제，재건립마이가부련상태천이개솔구진과정중，사용K-means취류산법화분예측치여진실치적차치상태구간，계이구건출우화예측산법。대산법진행료이론분석화수치실험，병여기타산법진행료비교，결과표명，해우화산법재선박정위수거단시예측영역구유교호적예측효과，우우다개기타산법，가응용우선박이동정위산품중。
In accordance with the characteristics of ship navigation positioning data based on time series, the transition probability feature of the Markov chain state is utilized to solve the predictive problem of relatively great random volatility based on autoregressive integrated moving average model. During the process of building the probability matrix of Markov chain state transition, the K-means clustering algorithm is used to divide the differential state interval between predicted value and true value, and then a set of optimized predictive algorithm model is built. The value experiment on the algorithm and its comparison with other algorithms show that this optimized algorithm has better predictive effect when it comes to short-term predication of the ship navigation positioning data. It is better than other algorithms and can be applied to mobile positioning products for ships.