Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.

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Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models

Applied Sciences ◽

10.3390/app11135853 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5853

Author(s):

Hyesook Son ◽

Seokyeon Kim ◽

Hanbyul Yeon ◽

Yejin Kim ◽

Yun Jang ◽

...

Keyword(s):

Deep Learning ◽

Visual Analysis ◽

Short Term Memory ◽

Learning Model ◽

Learning Models ◽

Visualization System ◽

Temporal Prediction ◽

Long Short Term Memory ◽

Gated Recurrent Units ◽

Deep Learning Model

The output of a deep-learning model delivers different predictions depending on the input of the deep learning model. In particular, the input characteristics might affect the output of a deep learning model. When predicting data that are measured with sensors in multiple locations, it is necessary to train a deep learning model with spatiotemporal characteristics of the data. Additionally, since not all of the data measured together result in increasing the accuracy of the deep learning model, we need to utilize the correlation characteristics between the data features. However, it is difficult to interpret the deep learning output, depending on the input characteristics. Therefore, it is necessary to analyze how the input characteristics affect prediction results to interpret deep learning models. In this paper, we propose a visualization system to analyze deep learning models with air pollution data. The proposed system visualizes the predictions according to the input characteristics. The input characteristics include space-time and data features, and we apply temporal prediction networks, including gated recurrent units (GRU), long short term memory (LSTM), and spatiotemporal prediction networks (convolutional LSTM) as deep learning models. We interpret the output according to the characteristics of input to show the effectiveness of the system.

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211018693 ◽

2021 ◽

pp. 036119812110186

Author(s):

Saeed Vasebi ◽

Yeganeh M. Hayeri ◽

Peter J. Jin

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Short Term Memory ◽

Data Availability ◽

Car Following ◽

Preceding Vehicle ◽

Long Short Term Memory ◽

The Right ◽

Mean Square Errors ◽

Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

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Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2021.3070959 ◽

2021 ◽

pp. 1-1

Author(s):

Seyed Amir Alavi ◽

Kamyar Mehran ◽

Vahid Vahidinasab ◽

Joao P. S. Catalao

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Consensus Control ◽

Term Memory ◽

Dc Microgrids ◽

Long Short Term Memory

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Efficient Deep Learning Models for DGA Domain Detection

Security and Communication Networks ◽

10.1155/2021/8887881 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Juhong Namgung ◽

Siwoon Son ◽

Yang-Sae Moon

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Ensemble Model ◽

Learning Models ◽

Short Term ◽

Domain Names ◽

Additional Information ◽

Domain Sequence ◽

Long Short Term Memory ◽

And Control

In recent years, cyberattacks using command and control (C&C) servers have significantly increased. To hide their C&C servers, attackers often use a domain generation algorithm (DGA), which automatically generates domain names for the C&C servers. Accordingly, extensive research on DGA domain detection has been conducted. However, existing methods cannot accurately detect continuously generated DGA domains and can easily be evaded by an attacker. Recently, long short-term memory- (LSTM-) based deep learning models have been introduced to detect DGA domains in real time using only domain names without feature extraction or additional information. In this paper, we propose an efficient DGA domain detection method based on bidirectional LSTM (BiLSTM), which learns bidirectional information as opposed to unidirectional information learned by LSTM. We further maximize the detection performance with a convolutional neural network (CNN) + BiLSTM ensemble model using Attention mechanism, which allows the model to learn both local and global information in a domain sequence. Experimental results show that existing CNN and LSTM models achieved F1-scores of 0.9384 and 0.9597, respectively, while the proposed BiLSTM and ensemble models achieved higher F1-scores of 0.9618 and 0.9666, respectively. In addition, the ensemble model achieved the best performance for most DGA domain classes, enabling more accurate DGA domain detection than existing models.

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Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network

IEEE Access ◽

10.1109/access.2020.3004284 ◽

2020 ◽

Vol 8 ◽

pp. 117365-117376

Author(s):

Qian Chen ◽

Wenyu Zhang ◽

Yu Lou

Keyword(s):

Neural Network ◽

Deep Learning ◽

Stock Prices ◽

Short Term Memory ◽

Learning Model ◽

Multi Layer Perceptron ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Deep Learning Model

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Jamming Prediction for Radar Signals Using Machine Learning Methods

Security and Communication Networks ◽

10.1155/2020/2151570 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Gyeong-Hoon Lee ◽

Jeil Jo ◽

Cheong Hee Park

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Training Data ◽

Electronic Warfare ◽

New Type ◽

Long Short Term Memory ◽

Radar Signals ◽

Feature Values ◽

Deep Learning Model

Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.

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Application of stacked and bidirectional long short-term memory deep learning models for wind speed forecasting at an offshore site

Energy Sources Part A Recovery Utilization and Environmental Effects ◽

10.1080/15567036.2021.1925379 ◽

2021 ◽

pp. 1-16

Author(s):

Bharat Kumar Saxena ◽

Sanjeev Mishra ◽

Komaragiri Venkata Subba Rao

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Wind Speed Forecasting ◽

Long Short Term Memory

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Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.4.733 ◽

2021 ◽

Vol 5 (4) ◽

pp. 380

Author(s):

Abdulkareem A. Hezam ◽

Salama A. Mostafa ◽

Zirawani Baharum ◽

Alde Alanda ◽

Mohd Zaki Salikon

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Denial Of Service ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Iot Devices

Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future. Even though many solutions have been developed to identify and prevent this assault, which is mainly targeted at IoT devices, the danger continues to exist and is now larger than ever. It is common practice to launch denial of service attacks in order to prevent legitimate requests from being completed. This is accomplished by swamping the targeted machines or resources with false requests in an attempt to overpower systems and prevent many or all legitimate requests from being completed. There have been many efforts to use machine learning to tackle puzzle-like middle-box problems and other Artificial Intelligence (AI) problems in the last few years. The modern botnets are so sophisticated that they may evolve daily, as in the case of the Mirai botnet, for example. This research presents a deep learning method based on a real-world dataset gathered by infecting nine Internet of Things devices with two of the most destructive DDoS botnets, Mirai and Bashlite, and then analyzing the results. This paper proposes the BiLSTM-CNN model that combines Bidirectional Long-Short Term Memory Recurrent Neural Network and Convolutional Neural Network (CNN). This model employs CNN for data processing and feature optimization, and the BiLSTM is used for classification. This model is evaluated by comparing its results with three standard deep learning models of CNN, Recurrent Neural Network (RNN), and long-Short Term Memory Recurrent Neural Network (LSTM–RNN). There is a huge need for more realistic datasets to fully test such models' capabilities, and where N-BaIoT comes, it also includes multi-device IoT data. The N-BaIoT dataset contains DDoS attacks with the two of the most used types of botnets: Bashlite and Mirai. The 10-fold cross-validation technique tests the four models. The obtained results show that the BiLSTM-CNN outperforms all other individual classifiers in every aspect in which it achieves an accuracy of 89.79% and an error rate of 0.1546 with a very high precision of 93.92% with an f1-score and recall of 85.73% and 89.11%, respectively. The RNN achieves the highest accuracy among the three individual models, with an accuracy of 89.77%, followed by LSTM, which achieves the second-highest accuracy of 89.71%. CNN, on the other hand, achieves the lowest accuracy among all classifiers of 89.50%.

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