# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
## 5 Conclusions  
This paper constructs a simple yet effective SSTP model, dubbed DSL, based on time-series similarity measure, multiple pattern learning and parameter optimization. We approach this complex issue through three stratagems: (1) Using Dynamic Time Warping (DTW) to mine the similarities in historical SST series. This can extract similar patterns of historical SST series simply and accurately. (2) Training an SVM using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences. Learning from multiple time-series sequences was instrumental to facilitating consistency enhancement and noise cancellation, thus achieving high prediction accuracy. (3) Developing an improved PSO method, dubbed LSPSO, to find the optimal parameters of the SSTP model, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency.  
Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading also to significant improvements in SST trend predictions. The efficiency LSPSO was verified to have better convergence and more uniform distribution by comparing with different optimization methods such as MODE, NSGA-II and OMOPSO. With the LSPSO, the SST prediction using SVM and DTW achieved a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieved a significant improvement in prediction accuracy of non-stationary SST time series compared to the more recent LSTM deep learning method. In general, our method provides a new way for marine operational forecasting.  
In the work carried out so far, we have assumed that SST changes were affected only by internal factors. As a future extension, we shall strive to take into account also other external factors (such as air temperature, air pressure, etc.). This will require considering the correlations between SST and these factors. We plan to use association rules to analyze impact on SST, and then use multi-factor prediction models to predict SST, striving for more comprehensive and robust predictions.  
Conceptualization, Q.H. and W.S.; methodology, C.Z., W.S., C.P. and A.L.; software, C.Z.; investigation, Y.D.; resources, Z.H.; writing--original draft preparation, Q.H. and C.Z.; writing--review and editing, W.S., C.P. and A.L.; supervision, Q.H. and W.S. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the National Key R&D Program of China (2016 YFC1401902), the National Natural Science Foundation of China (41671431), the Program for the Capacity Development for Shang Local Colleges (17050501900), the National Oceanic Administration Digital Ocean Science and Technology Key Laboratory Open Fund (B201801029) and the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR (QNHX1913).  
The sea surface temperature data were provided by State Key Laboratory of Satellite Ocean Environment Dynamics, the Second Institute of Oceanography, the Ministry of Natural Resources. The authors would also like to thank the anonymous reviewers for their very competent comments and helpful suggestions.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.