A generative adversarial network–based method for generating negative financial samples

In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distribution by the adversarial discriminator with time-sequence. Mapping data distribution to feature space is a common evaluation method of synthetic data; however, relationships between data attributes have been ignored in online transactions. We define a comprehensive evaluation method to evaluate the validity of generated samples from data distribution and attribute characteristics. Experimental results on real bank B2B transaction data show that the proposed model has higher overall ratings, which is 10% higher than traditional generation models. Finally, well-trained model is used to generate negative samples and form new dataset. The classification results on new datasets show that precision and recall are all higher than baseline models. Our work has a certain practical value and provides a new idea to solve imbalanced problem in whatever fields.

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Implementation of generative adversarial network-CLS combined with bidirectional long short-term memory for lithium-ion battery state prediction

Journal of Energy Storage ◽

10.1016/j.est.2020.101489 ◽

2020 ◽

Vol 31 ◽

pp. 101489 ◽

Cited By ~ 1

Author(s):

Haoliang Zhang ◽

Wei Tang ◽

Woonki Na ◽

Pyeong-Yeon Lee ◽

Jonghoon Kim

Keyword(s):

Lithium Ion Battery ◽

Short Term Memory ◽

Lithium Ion ◽

Short Term ◽

Generative Adversarial Network ◽

Term Memory ◽

Adversarial Network ◽

State Prediction ◽

Long Short Term Memory

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Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Sensors ◽

10.3390/s20247211 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7211

Author(s):

Kun Zhou ◽

Wenyong Wang ◽

Teng Hu ◽

Kai Deng

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Classification ◽

Training Process ◽

Short Term ◽

Generative Adversarial Network ◽

Convolutional Network ◽

Term Memory ◽

Adversarial Network ◽

Long Short Term Memory

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models’ effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of “Adam”. The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.

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Conditional LSTM-GAN for Melody Generation from Lyrics

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3424116 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-20

Author(s):

Yi Yu ◽

Abhishek Srivastava ◽

Simon Canales

Keyword(s):

Short Term Memory ◽

Generative Model ◽

Research Progress ◽

Short Term ◽

Large Dataset ◽

Generative Adversarial Network ◽

Term Memory ◽

Adversarial Network ◽

Challenging Research ◽

Long Short Term Memory

Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables us to learn and discover latent relationships between interesting lyrics and accompanying melodies. Unfortunately, the limited availability of a paired lyrics–melody dataset with alignment information has hindered the research progress. To address this problem, we create a large dataset consisting of 12,197 MIDI songs each with paired lyrics and melody alignment through leveraging different music sources where alignment relationship between syllables and music attributes is extracted. Most importantly, we propose a novel deep generative model, conditional Long Short-Term Memory (LSTM)–Generative Adversarial Network for melody generation from lyrics, which contains a deep LSTM generator and a deep LSTM discriminator both conditioned on lyrics. In particular, lyrics-conditioned melody and alignment relationship between syllables of given lyrics and notes of predicted melody are generated simultaneously. Extensive experimental results have proved the effectiveness of our proposed lyrics-to-melody generative model, where plausible and tuneful sequences can be inferred from lyrics.

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Locational Marginal Price Forecasting Using Convolutional Long-Short Term Memory-Based Generative Adversarial Network

10.1109/pesgm46819.2021.9637861 ◽

2021 ◽

Author(s):

Zhongxia Zhang ◽

Meng Wu

Keyword(s):

Short Term Memory ◽

Price Forecasting ◽

Short Term ◽

Generative Adversarial Network ◽

Term Memory ◽

Adversarial Network ◽

Locational Marginal Price ◽

Long Short Term Memory ◽

Marginal Price

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Drastic Fluctuation Prediction of the Number of Comments on Social Media by Attributes of Comments

International Journal of Semantic Computing ◽

10.1142/s1793351x2150001x ◽

2021 ◽

Vol 15 (01) ◽

pp. 1-21

Author(s):

Yuan Wang ◽

Guan-Shen Fang ◽

Sayaka Kamei

Keyword(s):

Social Media ◽

Short Term Memory ◽

Real Data ◽

Web Content ◽

Short Term ◽

Social Responses ◽

Term Memory ◽

Online Social Media ◽

Relevant Topic ◽

Long Short Term Memory

Online social media has an exponential level of communication speed in terms of message dissemination. Users can publish comments freely to various web content on a characteristic network of communicators and viewers. Many of these comments contain emotions or opinions of users, which may cause sympathy and influence others’ comments. Moreover, such comments may raise social responses, i.e. they may cause drastic fluctuations in the number of comments. In this study, using the content of textual comments, we propose two structural approaches (PDFCPL and PDFCML) to predict the future drastic fluctuation in the number of comments based on Long Short-Term Memory (LSTM). To quantify each textual comment, we define two attributes: (1) relevance to its relevant topic based on cosine similarity and (2) importance of its content which is calculated by TF-IDF. The predictions are made by these attributes and the number of previously observed comments as well. To evaluate the performance of our approaches, we conduct comparing experiments with other methods on real data of Twitter. The results present that the proposed method PDFCPL has better performance than existing methods to predict the occurrence of drastic fluctuation in the number of comments.

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The development of a sepedi text generation model using long-short term memory

Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications ◽

10.1145/3415088.3415114 ◽

2020 ◽

Author(s):

Mercy M. Moila ◽

Thipe I. Modipa

Keyword(s):

Short Term Memory ◽

Generation Model ◽

Text Generation ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative adversarial neural network

F1000Research ◽

10.12688/f1000research.22907.1 ◽

2020 ◽

Vol 9 ◽

pp. 298 ◽

Cited By ~ 1

Author(s):

Sari Sabban ◽

Mikhail Markovsky

Keyword(s):

Protein Design ◽

Short Term Memory ◽

De Novo ◽

Protein Structures ◽

Protein Backbone ◽

Short Term ◽

Generative Adversarial Network ◽

Term Memory ◽

Helical Protein ◽

Long Short Term Memory

The ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design ofjust the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) generative adversarial network (GAN) architecture. The LSTM-based GAN model used only theφandψangles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact,helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM-GAN architecture using only theφandψangles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.

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Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2138/1/012015 ◽

2021 ◽

Vol 2138 (1) ◽

pp. 012015

Author(s):

Haifei Zhang ◽

Jian Xu ◽

Lanmei Qian ◽

Jianlin Qiu

Keyword(s):

Short Term Memory ◽

Real Data ◽

Development Trend ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network ◽

One Year ◽

The Masses ◽

The Development Trend

Abstract The sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China’s development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation.

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