{"title":"用于提取特征以预测股市趋势的优化深度学习模型架构","authors":"N. Deepika, M. NirupamaBhat","doi":"10.5750/ijme.v1i1.1338","DOIUrl":null,"url":null,"abstract":"Predicting stock trends is a complex task influenced by various factors such as market sentiment, economic indicators, and company performance. Analysts often employ technical analysis, studying historical price patterns and trading volumes, as well as fundamental analysis, assessing financial statements and industry trends. Deep Learning models have also gained popularity for predicting stock trends, using algorithms to identify patterns and relationships in large datasets. Deep learning algorithms, particularly neural networks, excel at recognizing intricate patterns and relationships within complex datasets, making them well-suited for predicting stock prices, identifying trends, and managing risk. Hence, this paper proposed a Bird Swarm Optimization ARIMA LSTM (BSO-ARIMA-DL) model for stock trend prediction. The proposed BSO-ARIMA-DL model performance is applied in the company datasets Apple, Amazon, and Infosys for stock trend prediction. With the proposed BSO-ARIMA-DL model features are optimized for the identification of features in the dataset for the evaluation of optimal features. Upon the estimation of features, the ARIMA model with the LSTM architecture is implemented for the stock trend analysis. The proposed BSO-ARIMA-DL model deep learning model is implemented for the stock trend prediction in the companies. The results demonstrated that the proposed BSO-ARIMA-DL model exhibits a minimal error of ~10% minimal to the conventional ARIMA model.","PeriodicalId":50313,"journal":{"name":"International Journal of Maritime Engineering","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimized Deep Learning Model Architecture for the Feature Extraction to Predict Trend in Stock Market\",\"authors\":\"N. Deepika, M. NirupamaBhat\",\"doi\":\"10.5750/ijme.v1i1.1338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting stock trends is a complex task influenced by various factors such as market sentiment, economic indicators, and company performance. Analysts often employ technical analysis, studying historical price patterns and trading volumes, as well as fundamental analysis, assessing financial statements and industry trends. Deep Learning models have also gained popularity for predicting stock trends, using algorithms to identify patterns and relationships in large datasets. Deep learning algorithms, particularly neural networks, excel at recognizing intricate patterns and relationships within complex datasets, making them well-suited for predicting stock prices, identifying trends, and managing risk. Hence, this paper proposed a Bird Swarm Optimization ARIMA LSTM (BSO-ARIMA-DL) model for stock trend prediction. The proposed BSO-ARIMA-DL model performance is applied in the company datasets Apple, Amazon, and Infosys for stock trend prediction. With the proposed BSO-ARIMA-DL model features are optimized for the identification of features in the dataset for the evaluation of optimal features. Upon the estimation of features, the ARIMA model with the LSTM architecture is implemented for the stock trend analysis. The proposed BSO-ARIMA-DL model deep learning model is implemented for the stock trend prediction in the companies. The results demonstrated that the proposed BSO-ARIMA-DL model exhibits a minimal error of ~10% minimal to the conventional ARIMA model.\",\"PeriodicalId\":50313,\"journal\":{\"name\":\"International Journal of Maritime Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Maritime Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5750/ijme.v1i1.1338\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Maritime Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5750/ijme.v1i1.1338","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Optimized Deep Learning Model Architecture for the Feature Extraction to Predict Trend in Stock Market
Predicting stock trends is a complex task influenced by various factors such as market sentiment, economic indicators, and company performance. Analysts often employ technical analysis, studying historical price patterns and trading volumes, as well as fundamental analysis, assessing financial statements and industry trends. Deep Learning models have also gained popularity for predicting stock trends, using algorithms to identify patterns and relationships in large datasets. Deep learning algorithms, particularly neural networks, excel at recognizing intricate patterns and relationships within complex datasets, making them well-suited for predicting stock prices, identifying trends, and managing risk. Hence, this paper proposed a Bird Swarm Optimization ARIMA LSTM (BSO-ARIMA-DL) model for stock trend prediction. The proposed BSO-ARIMA-DL model performance is applied in the company datasets Apple, Amazon, and Infosys for stock trend prediction. With the proposed BSO-ARIMA-DL model features are optimized for the identification of features in the dataset for the evaluation of optimal features. Upon the estimation of features, the ARIMA model with the LSTM architecture is implemented for the stock trend analysis. The proposed BSO-ARIMA-DL model deep learning model is implemented for the stock trend prediction in the companies. The results demonstrated that the proposed BSO-ARIMA-DL model exhibits a minimal error of ~10% minimal to the conventional ARIMA model.
期刊介绍:
The International Journal of Maritime Engineering (IJME) provides a forum for the reporting and discussion on technical and scientific issues associated with the design and construction of commercial marine vessels . Contributions in the form of papers and notes, together with discussion on published papers are welcomed.