The Offset Azimuth Prediction of Light Buoy Based on Markov Chain Optimization Multiplicative Seasonal Model

Abstract

Aimed at the problem of the large error caused by uncertain factors in the fitting process of the traditional multiplicative seasonal model, the advantages of the Markov chain in this study are applied to the multiplicative seasonal model to optimize the prediction results. Based on the residual value between the theoretical and actual values, the values of different intervals are divided into states. The transition probability matrix is established through different probabilities; then, the weighted sum of different prediction probabilities is carried out to select the optimal prediction state. The real number of Meizhou Bay portlight buoys is used to verify the prediction effect of the model, and MAE, MAPE, RMSE, RRMSE, SSE, R2 are used to calculate the error between the predicted value and the actual value. The results show that compared to the traditional multiplicative seasonal model and other prediction models, the prediction MAE of the MC-SARIMA model is decreased by 2.19003794, the MAPE is decreased by 0.66%, the RMSE is decreased by 2.092671823, the RRMSE is decreased by 0.006221352, the SSE is decreased by 404.0231931, and the R2 is increased by 0.224686247. It shows that the multiplicative seasonal model optimized by the Markov chain can predict the azimuth data of the light buoy more effectively than the traditional multiplicative seasonal model and other prediction models.

The Offset Position Prediction of Light Buoy Based on Grey Optimization Multiplicative Seasonal Model

“…e caisson position of the buoy is (119.046 °E, 24.915 °N). e calculation formula is shown in equation (15), and some calculation results are shown in Table 1.…”

“…According to equation ( 14), the two theoretical values are weighted to obtain w SARIMA � 28.49% and w GM(1,1) � 71.54%. According to equation (15), the offset distance of the light buoy from 13:00 to 24:00 on April 6, 2019, is predicted. e prediction results are shown in Figure 7.…”

“…Tian et al [14] proposed a prediction approach for short-term wind speed using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine; the results show that the prediction approach in this paper has higher reliability under the same confidence level. Wu et al [15] proposed the MC-SARIMA prediction model; the experimental results show that the prediction error of this model is low. e above-related studies have achieved good application results by using different methods to predict the trajectory and position of mobile terminals or personnel, but there are also some shortcomings.…”