Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
A sheer number of techniques and web resources are available for software engineering practice and this number continues to grow.Discovering semantically similar or related technical terms and web resources offers the...A sheer number of techniques and web resources are available for software engineering practice and this number continues to grow.Discovering semantically similar or related technical terms and web resources offers the opportunity to design appealing services to facilitate information retrieval and information discovery.In this study,we extract technical terms and web resources from a community of question and answer(Q&A)discussions and propose an approach based on a neural language model to learn the semantic representations of technical terms and web resources in a joint low-dimensional vector space.Our approach maps technical terms and web resources to a semantic vector space based only on the surrounding technical terms and web resources of a technical term(or web resource)in a discussion thread,without the need for mining the text content of the discussion.We apply our approach to Stack Overflow data dump of March 2018.Through both quantitative and qualitative analyses in the clustering,search,and semantic reasoning tasks,we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of technical terms and web resources,and they can be exploited to support various search and semantic reasoning tasks,by means of simple K-nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.展开更多
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
基金the National Natural Science Foundation of China(No.61872232)。
文摘A sheer number of techniques and web resources are available for software engineering practice and this number continues to grow.Discovering semantically similar or related technical terms and web resources offers the opportunity to design appealing services to facilitate information retrieval and information discovery.In this study,we extract technical terms and web resources from a community of question and answer(Q&A)discussions and propose an approach based on a neural language model to learn the semantic representations of technical terms and web resources in a joint low-dimensional vector space.Our approach maps technical terms and web resources to a semantic vector space based only on the surrounding technical terms and web resources of a technical term(or web resource)in a discussion thread,without the need for mining the text content of the discussion.We apply our approach to Stack Overflow data dump of March 2018.Through both quantitative and qualitative analyses in the clustering,search,and semantic reasoning tasks,we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of technical terms and web resources,and they can be exploited to support various search and semantic reasoning tasks,by means of simple K-nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.
