scholarly journals Improved Multi-Scale Deep Integration Paradigm for Point and Interval Carbon Trading Price Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2595
Author(s):  
Jujie Wang ◽  
Shiyao Qiu
Keyword(s):  
Short Term Memory ◽  
Prediction Interval ◽  
Statistical Error ◽  
Interval Estimate ◽  
Carbon Trading ◽  
Integration Model ◽  
Reconstruction Process ◽  
Multi Scale ◽  
Original Dataset ◽  
Residual Part

The forecast of carbon trading price is crucial to both sellers and purchasers; multi-scale integration models have been used widely in this process. However, these multi-scale models ignore the feature reconstruction process as well as the residual part and also they often focus on the linear integration. Meanwhile, most of the models cannot provide prediction interval which means they neglect the uncertainty. In this paper, an improved multi-scale nonlinear integration model is proposed. The original dataset is divided into some subgroups through variational mode decomposition (VMD) and all the subgroups will go through sample entropy (SE) process to reconstruct the features. Then, random forest and long-short term memory (LSTM) integration are used to model feature sub-sequences. For the residual part, LSTM residual correction strategy based on white noise test corrects residuals to obtain point prediction results. Finally, Gaussian process (GP) is applied to get the prediction interval estimate. The result shows that compared with some other methods, the proposed method can obtain satisfying accuracy which has the minimum statistical error. So, it is safe to conclude that the proposed method is able to efficiently predict the carbon price as well as to provide the prediction interval estimate.

scholarly journals Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective

2019 ◽  
Vol 8 (9) ◽  
pp. 417 ◽  
Author(s):  
Wei Cui ◽  
Dongyou Zhang ◽  
Xin He ◽  
Meng Yao ◽  
Ziwei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Receptive Field ◽  
Large Scale ◽  
Semantic Information ◽  
Short Term Memory ◽  
Global Perspective ◽  
Small Scale ◽  
Multi Scale ◽  
Memory Network ◽  
Long Short Term Memory

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions.

scholarly journals A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling

Forecasting ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 1-25
Author(s):  
Thabang Mathonsi ◽  
Terence L. van Zyl
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Prediction Interval ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Exponential Smoothing ◽  
Learning Methods

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction.

scholarly journals Characterizing the time course of decision-making in change detection

2021 ◽  
Author(s):  
Anthea Grace Blunden ◽  
Dylan Hammond ◽  
Piers Howe ◽  
Daniel R. Little
Keyword(s):  
Change Detection ◽  
Time Course ◽  
Short Term Memory ◽  
Large Set ◽  
Short Term ◽  
Modeling Framework ◽  
Integration Model ◽  
First Order ◽  
Visual Short Term Memory ◽  
Or Task

We propose a novel modeling framework for characterizing the time course of change detection based on information held in visual short-term memory. Specifically, we seek to answer whether change detection is better captured by a first-order integration model, in which information is pooled from each location, or a second-order integration model, in which each location is processed independently. We diagnose whether change detection across locations proceeds in serial or parallel and how processing is affected by the stopping rule (i.e., detecting any change versus detecting all changes; Experiment 1) and how the efficiency of detection is affected by the number of changes in the display (Experiment 2). We find that although capacity is generally limited in both tasks, architecture varies from parallel self-terminating in the OR task to serial self-terminating in the AND task. Our novel framework allows model comparisons across a large set of models ruling out several competing explanations of change detection.

scholarly journals Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 139
Author(s):  
Yongjie Ping ◽  
Chao Chen ◽  
Lu Wu ◽  
Yinglong Wang ◽  
Minglei Shu
Keyword(s):  
Atrial Fibrillation ◽  
Neural Networks ◽  
Short Term Memory ◽  
Segment Length ◽  
Practical Applications ◽  
Multi Scale ◽  
Long Short Term Memory ◽  
Electrocardiogram Ecg ◽  
Fold Cross Validation

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.

scholarly journals Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1881 ◽  
Author(s):  
Xiaorui Shao ◽  
Chang-Soo Kim ◽  
Palash Sontakke
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Sensor Data ◽  
Short Term ◽  
Multi Scale ◽  
Deep Model ◽  
Cost Of Time ◽  
The Impact

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting.

scholarly journals Fault Diagnosis of Main Pump in Converter Station Based on Deep Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1284
Author(s):  
Qingsheng Zhao ◽  
Gong Cheng ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Cooling System ◽  
Imbalanced Data ◽  
Maintenance Cost ◽  
Stable Operation ◽  
Vibration Signals ◽  
Multi Scale ◽  
Temporal Features

As the core component of the valve cooling system in a converter station, the main pump plays a major role in ensuring the stable operation of the valve. Thus, accurate and efficient fault diagnosis of the main pump according to vibration signals is of positive significance for the detection of failure equipment and reducing the maintenance cost. This paper proposed a new neural network based on the vibration signals of the main pump to classify four faults and one normal state of the main pump, which consisted of a convolutional neural network (CNN) and long short-term memory (LSTM). Multi-scale features were extracted by two CNNs with different kernel sizes, and temporal features were extracted by LSTM. Moreover, random sampling was used in data processing for imbalanced data, which is meaningful for data symmetry. Experimental results indicated that the accuracy of the network was 0.987 obtained from the test set, and the average values of F1-score, recall, and precision were 0.987, 0.987, and 0.988, respectively. It was found that the proposed network performed well in a multi-label fault diagnosis of the main pump and was superior to other methods.

scholarly journals Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fang Yao ◽  
Wei Liu ◽  
Xingyong Zhao ◽  
Li Song
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Statistical Approach ◽  
Short Term Memory ◽  
Prediction Interval ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Power Forecasting

This paper develops an integrated machine learning and enhanced statistical approach for wind power interval forecasting. A time-series wind power forecasting model is formulated as the theoretical basis of our method. The proposed model takes into account two important characteristics of wind speed: the nonlinearity and the time-changing distribution. Based on the proposed model, six machine learning regression algorithms are employed to forecast the prediction interval of the wind power output. The six methods are tested using real wind speed data collected at a wind station in Australia. For wind speed forecasting, the long short-term memory (LSTM) network algorithm outperforms other five algorithms. In terms of the prediction interval, the five nonlinear algorithms show superior performances. The case studies demonstrate that combined with an appropriate nonlinear machine learning regression algorithm, the proposed methodology is effective in wind power interval forecasting.

Sign in / Sign up

Export Citation Format

Share Document