关键词: 差分自回归移动平均模型（ARIMA）, 马尔可夫链, K-means, 定位数据预测

Abstract: 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.

Key words: autoregressive integrated moving average model (ARIMA), Markov chain, K-means, positioning data prediction