scholarly journals Forecasting Stock Price using LSTM-CNN Method

2021 ◽  
Vol 11 (1) ◽  
pp. 1-8
Author(s):  
Dinesh Reddy ◽  
◽  
Abhinav Karthik ◽  
Keyword(s):  
Stock Price ◽  
Single Model ◽  
Neural Organization ◽  
Proposed Model ◽  
Similar Information ◽  
The Ideal

Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

scholarly journals Ensemble of Deep Capsule Neural Networks: An Application to Pneumonia Prediction

2022 ◽  
Author(s):  
Jyostna Bodapati ◽  
Rohith V N ◽  
Venkatesulu Dondeti
Keyword(s):  
Performance Measures ◽  
Laboratory Testing ◽  
Deep Neural Network ◽  
Routing Algorithm ◽  
Experimental Studies ◽  
Single Model ◽  
Appropriate Treatment ◽  
Pediatric Pneumonia ◽  
Proposed Model ◽  
Pneumonia Diagnosis

Abstract Pneumonia is the primary cause of death in children under the age of 5 years. Faster and more accurate laboratory testing aids in the prescription of appropriate treatment for children suspected of having pneumonia, lowering mortality. In this work, we implement a deep neural network model to efficiently evaluate pediatric pneumonia from chest radio graph images. Our network uses a combination of convolutional and capsule layers to capture abstract details as well as low level hidden features from the the radio graphic images, allowing the model to generate more generic predictions. Furthermore, we combine several capsule networks by stacking them together and connected them with dense layers. The joint model is trained as a single model using joint loss and the weights of the capsule layers are updated using the dynamic routing algorithm. The proposed model is evaluated using benchmark pneumonia dataset\cite{kermany2018identifying}, and the outcomes of our experimental studies indicate that the capsules employed in the network enhance the learning of disease level features that are essential in diagnosing pneumonia. According to our comparison studies, the proposed model with Convolution base from InceptionV3 attached with Capsule layers at the end surpasses several existing models by achieving an accuracy of 94.84\%. The proposed model is superior in terms of various performance measures such as accuracy and recall, and is well suited to real-time pediatric pneumonia diagnosis, substituting manual chest radiography examination.

scholarly journals Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

2020 ◽  
Author(s):  
Victor Biazon ◽  
Reinaldo Bianchi
Keyword(s):  
Stock Market ◽  
Wavelet Transforms ◽  
Stock Price ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Time Step ◽  
Proposed Model ◽  
Gated Recurrent Unit ◽  
Novel Model ◽  
Unit Network

Trading in the stock market always comes with the challenge of deciding the best action to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it. In this work we propose a novel model called Discrete Wavelet Transform Gated Recurrent Unit Network (DWT-GRU). The model learns from the data to choose between buying, holding and selling, and when to execute them. The proposed model was compared to other recurrent neural networks, with and without wavelets preprocessing, and the buy and hold strategy. The results shown that the DWT-GRU outperformed all the set baselines in the analysed stocks of the Brazilian stock market.

scholarly journals Econometric model for forecasting oil production in OECD member states

2020 ◽  
Vol 159 ◽  
pp. 02005
Author(s):  
Carmen Valentina Rădulescu ◽  
Dumitru Alexandru Bodislav ◽  
Sorin Burlacu ◽  
Florina Bran ◽  
Lyaman Karimova
Keyword(s):  
Financial Markets ◽  
Econometric Model ◽  
Oil Production ◽  
Decision Makers ◽  
Production Forecast ◽  
Member States ◽  
Proposed Model ◽  
Oil And Natural Gas ◽  
Social Environmental ◽  
The Ideal

In this article we present an econometric model of oil production forecast at OECD member level that will allow decision makers but also other oil product stakeholders to be responsible for oil production in OECD member states. This responsibility can be perceived from several perspectives: economic, social, environmental, political, military etc. In order to be able to find the ideal formula for our calculation, we went through the specialized literature and brought elements of analysis during the research through several econometric paths traveled by other researchers and who provided us with support for our research. Before proceeding technically, in order to understand the urgency of this approach and of this study, we also discussed how oil and natural gas are explored, exploited and extracted from the underground deposits. We considered that the proposed model could be improved in the future so as to portray certain geopolitical or economic factors, determinants for oil production, such as embargoes, periods of armed conflict in the main extraction areas or times of financial crisis and the decline of financial markets.

Time-changed generalized mixed fractional Brownian motion and application to arithmetic average Asian option pricing

2017 ◽  
Vol 6 (3) ◽  
pp. 85
Author(s):  
ömer önalan
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Stock Price ◽  
Long Range Dependence ◽  
Price Process ◽  
Inverse Gamma ◽  
Proposed Model ◽  
Mixed Fractional Brownian Motion ◽  
Time Periods

In this paper we present a novel model to analyze the behavior of random asset price process under the assumption that the stock price pro-cess is governed by time-changed generalized mixed fractional Brownian motion with an inverse gamma subordinator. This model is con-structed by introducing random time changes into generalized mixed fractional Brownian motion process. In practice it has been observed that many different time series have long-range dependence property and constant time periods. Fractional Brownian motion provides a very general model for long-term dependent and anomalous diffusion regimes. Motivated by this facts in this paper we investigated the long-range dependence structure and trapping events (periods of prices stay motionless) of CSCO stock price return series. The constant time periods phenomena are modeled using an inverse gamma process as a subordinator. Proposed model include the jump behavior of price process because the gamma process is a pure jump Levy process and hence the subordinated process also has jumps so our model can be capture the random variations in volatility. To show the effectiveness of proposed model, we applied the model to calculate the price of an average arithmetic Asian call option that is written on Cisco stock. In this empirical study first the statistical properties of real financial time series is investigated and then the estimated model parameters from an observed data. The results of empirical study which is performed based on the real data indicated that the results of our model are more accuracy than the results based on traditional models.

scholarly journals SELECTING THE OPTIMAL RENEWABLE ENERGY USING MULTI CRITERIA DECISION MAKING

2013 ◽  
Vol 14 (5) ◽  
pp. 957-978 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini ◽  
Mohammad Majid Fouladgar ◽  
Edmundas Kazimieras Zavadskas ◽  
S. Hamzeh Haji Moini
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Evaluation Criteria ◽  
Energy Resources ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Proposed Model ◽  
Negative Ideal Solution ◽  
Hierarchy Process ◽  
The Ideal

Renewable energies are well-known as one of the most important energy resources not only due to limited other energy resources, but also due to environmental problems associated with air pollutants and greenhouse gas emissions. Renewable energy project selection is a multi actors and sophisticated problem because it is a need to incorporate social, economic, technological, and environmental considerations. Multi criteria decision making (MCDM) methods are powerful tools to evaluate and rank the alternatives among a pool of alternatives and select the best one. COPRAS (COmplex PRoportional ASsessment) is an MCDM technique which determines the best alternative by calculating the ratio to the ideal solution and the negative ideal solution. On the other hand, analytical hierarchy process (AHP) is widely used in order to calculate the importance weights of evaluation criteria. In this paper an integrated COPRAS-AHP methodology is proposed to select the best renewable energy project. In order to validate the output of the proposed model, the model is compared with five MCDM tools. The results of this paper demonstrate the capability and effectiveness of the proposed model in selecting the most appropriate renewable energy option among the existing alternatives.

Post-modern Career Counselling to a Gifted Black Youth: A Case Study

2001 ◽  
Vol 15 (3) ◽  
pp. 325-339 ◽  
Author(s):  
J.G. Maree ◽  
S.E. Bester ◽  
C. Lubbe ◽  
G. Beck
Keyword(s):  
South African ◽  
Life Story ◽  
Career Counselling ◽  
Modern Approach ◽  
Primary Responsibility ◽  
Proposed Model ◽  
Consultative Process ◽  
Modern Perspective ◽  
The Ideal

It has become critically imperative that career counselling be made accessible to the majority of the South African population. At the same time it has to continue to address the needs and diversity of individual learners. This article attempts to illustrate the potential and flexibility of a post-modern model for career counselling. Career counselling from a post-modern perspective requires reconsidering the traditional modern approach of the 20th century. Increasing disillusionment with modernism because of unfulfilled dreams and ideals have resulted in a change of approach to career counselling that corresponds with the post-modern discourse. The change of focus has been one from ‘matching to the ‘empowerment’ of clients, not only to make career choices, but also to accept primary responsibility for these decisions. The needs of the client come first with the sole view of empowering him/her to make his/her own decisions about the future. A narrative approach is adopted by which the client creates hislher own life story, with a view to creating an ideal story as close to the ideal as possible. This model, which progresses through three phases, inter alia, comprises a consultative process of career counselling to all learners, irrespective of race, gender, age or culture. The article is highlighted by the presentation of a case study in which the proposed model for post-modern career counselling is put into practice by administering counselling to a gifted black child.

scholarly journals Depthwise Convolution Is All You Need for Learning Multiple Visual Domains

2019 ◽  
Vol 33 ◽  
pp. 8368-8375 ◽  
Author(s):  
Yunhui Guo ◽  
Yandong Li ◽  
Liqiang Wang ◽  
Tajana Rosing
Keyword(s):  
State Of The Art ◽  
The State ◽  
Spatial Correlations ◽  
Single Model ◽  
Domain Specific ◽  
Gating Mechanism ◽  
Proposed Model ◽  
Domain Learning ◽  
Domain Models ◽  
Universal Structure

There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.

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