LSTM based long-term energy consumption prediction with periodicity

Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.

A CFCC-LSTM Model for Sea Surface Temperature Prediction

El Nino-Southern Oscillation (ENSO), which is one of the main drivers of Earth's inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of ENSO is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Nino 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Nino 3.4 index and SOI mix many low- and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. Therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (EEMD-TCN) hybrid approach, which decomposes the highly variable Nino 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Nino 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984-2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Nino 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Nino 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and EEMD-LSTM methods showed that the EEMD-TCN provides the best overall prediction of both the Nino 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. This result means that the TCN approach performs well in the advance prediction of ENSO and will be of great guiding significance in studying it.

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Temporal Convolutional Networks for the Advance Prediction of ENSO.

Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

The classification in class imbalanced data has drawn significant interest in medical application. Most existing methods are prone to categorize the samples into the majority class, resulting in bias, in particular the insufficient identification of minority class. A kind of novel approach, class weights random forest is introduced to address the problem, by assigning individual weights for each class instead of a single weight. The validation test on UCI data sets demonstrates that for imbalanced medical data, the proposed method enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class.

Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean The SST prediction problem is formulated as a time series regression problem The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer The LSTM layer is utilized to model the time series relationship The full-connected layer is utilized to map the output of the LSTM layer to a final prediction The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method The prediction accuracy is also tested on the SST anomaly data In addition, the model’s online updated characteristics are presented

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Random Forests are powerful classification and regression tools that are commonly applied in machine learning and image processing. In the majority of random classification forests algorithms, the Gini index and the information gain ratio are commonly used for node splitting. However, these two kinds of node-split methods may pay less attention to the intrinsic structure of the attribute variables and fail to find attributes with strong discriminate ability as a group yet weak as individuals. In this paper, we propose an innovative method for splitting the tree nodes based on the cooperative game theory, from which some attributes with good discriminate ability as a group can be learned. This new random forests algorithm is called Cooperative Profit Random Forests (CPRF). Experimental comparisons with several other existing random classification forests algorithms are carried out on several real-world data sets, including remote sensing images. The results show that CPRF outperforms other existing Random Forests algorithms in most cases. In particular, CPRF achieves promising results in ocean front recognition.

Cooperative Profit Random Forests With Application in Ocean Front Recognition

Random Multi-Graphs

Spectral clustering is one of the most popular and important clustering methods in pattern recognition, machine learning, and data mining. However, its high computational complexity limits it in applications involving truly large-scale datasets. For a clustering problem with n samples, it needs to compute the eigenvectors of the graph Laplacian with O(n3) time complexity. To address this problem, we propose a novel method called anchor-based spectral clustering (ASC) by employing anchor points of data. Specifically, m (m ≪ n) anchor points are selected from the dataset, which can basically maintain the intrinsic (manifold) structure of the original data. Then a mapping matrix between the original data and the anchors is constructed. More importantly, it is proved that this data-anchor mapping matrix essentially preserves the clustering structure of the data. Based on this mapping matrix, it is easy to approximate the spectral embedding of the original data. The proposed method scales linearly relative to the size of the data but with low degradation of the clustering performance. The proposed method, ASC, is compared to the classical spectral clustering and two state-of-the-art accelerating methods, i.e., power iteration clustering and landmark-based spectral clustering, on 10 real-world applications under three evaluation metrics. Experimental results show that ASC is consistently faster than the classical spectral clustering with comparable clustering performance, and at least comparable with or better than the state-of-the-art methods on both effectiveness and efficiency.

An anchor-based spectral clustering method

With advanced observation instruments, such as satellite radars and altimeters, huge amounts of oceanic data can be measured and saved everyday. How to extract effective information from these raw data becomes an urgent problem in the research of ocean science. In this chapter, we review the data representation learning algorithms, which try to learn effective features from raw data and deliver high prediction accuracy for the unseen data. Particularly, we describe two pieces of work to show how the state-of-the-art deep learning models can be applied to oceanic data analysis. That is how the deep convolutional neural networks (CNNs) are used for ocean front recognition and how the long short term memory (LSTM) networks are employed for sea surface temperature prediction. We believe that these two pieces of work are interesting to the researchers in both the machine learning and the ocean science areas, and many machine learning algorithms will be adopted in the ocean science applications.

Qin Zhang

Papers

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Cooperative Profit Random Forests With Application in Ocean Front Recognition

Random Multi-Graphs

An anchor-based spectral clustering method

Oceanic Data Analysis with Deep Learning Models