Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network

Xu, Lingyu; Qin, Li; Yu, Jie; Wang, Lei; Xie, Jiang; Shi, Suixiang

doi:10.1080/01431161.2019.1701724

Cited by 27 publications

(19 citation statements)

References 28 publications

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“…The layer number of the three-dimensional matrix was m, which equaled the number of samples. The numbers of convolution kernels were 8 and 24, dilations = (1,2,4,8,16,32,64,128,256), and stack = 1. A preliminary experiment showed that when the number of convolution kernels was set to 8, the model showed optimal efficiency, indicating that when training the model, at least the information of the past 8 months needed to be considered, and as the dilation factor increased, the historical time to be considered should also increase.…”

Section: Methodsmentioning

confidence: 99%

“…The prediction of sea surface temperature is usually solved as a time series problem, usually using LSTM. But LSTM has two disadvantages: it cannot extract spatial features and is prone to gradient problems when there is too much data [15,16]. By contrast, the TCN model can extract features in both time and space and is not prone to gradient problems because of its structure.…”

Section: Introductionmentioning

confidence: 99%

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High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things

Sun

Feng

et al. 2022

Sensors

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Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and Temporal Convolutional Network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things

Sun

Feng

et al. 2022

Sensors

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“…Wi, Wf, Wo, Wg, bi, bf, bo and bg are coefficient matrixes. Via the function of the different gates, LSTM memory units can capture the complex correlation features within time series in both short and long term, which is a remarkable improvement compared with RNN [23], [24].…”

Section: B Lstmmentioning

confidence: 99%

Analysis and Application of Grey Wolf Optimizer-Long Short-Term Memory

et al. 2020

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Long short-term memory (LSTM) is widely applied in both academic and industrial fields. However, there is no reliable criterion on selecting hyperparameters of LSTM. Currently, although some widely used classic methods such as random search and grid search have obtained success to some extent, the problems in local optimum and convergence still exist. In this research, we propose to use grey wolf optimizer (GWO) to search for the hyperparameters of LSTM. Through the method, the superiority of metaheuristic in global optimization and the strength of LSTM in predicting are combined. In this model, number of hidden layer nodes and learning rate of LSTM are set as preys, and grey wolf pack has a simple but efficient mechanism to search for the optimal hyperparameters. The benchmark tests on several basic functions were utilized, and the results were verified by a comparative study with random search, support vector regression and several other regression methods. Specifically, we applied this algorithm in predicting the degradation trend of the airborne fuel pump. As a result, the ergodicity and convergence of the algorithm are proved mathematically based on Markov processes theory. The benchmark tests show that the GWO-LSTM model holds for predicting data with low overall slope and high partial fluctuation. The application in airborne fuel pump shows that, trained by dataset with 5700 points, the proposed model could predict sequence of 300 points with root mean square error 0.617 after 30 iterations of optimizing, which is 2.512 previously. The result further demonstrates that the proposed algorithm is applicable to make prediction with high accuracy. Overall, the effectiveness of GWO-LSTM model is verified from theoretical proof to benchmark tests and then to actual product application.

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“…The prediction of time series aims at estimating their future trends with hidden characteristics in the historical data. Time series prediction has gained widespread attention in many fields, such as meteorology [1,2], the stock market [3], environment pollution control [4,5], and data mining on the Internet. The reliable prediction of future trends can help administrators in comprehensive and scientific decision making [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series

Shi

Bai

Wang

et al. 2022

Entropy

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The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.

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Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network

Cited by 27 publications

References 28 publications

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things

Analysis and Application of Grey Wolf Optimizer-Long Short-Term Memory

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series

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