FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281558554> ?p ?o ?g. }

• W4281558554 endingPage "600" @default.
• W4281558554 startingPage "573" @default.
• W4281558554 abstract "• This paper presents a novel Deep Learning approach for time series prediction . • This paper develops a hybridization of poly-linear regression with LSTM and data augmentation. • The proposed PLR-ALSTM-NN is well-grounded against a few state-of-the-art DL approaches. Until recently, the supply chain sector, which had been getting by with scattered spreadsheets, phone conversations, and even paper-based records until recently, was exposed for its antiquated methods during the epidemic. As a result, businesses have undergone a decade of digital change in only a few months, with the epidemic driving them to replace antiquated procedures with AI, machine learning, and data science technology. The supply chain sector has reached a point in its AI adoption where the technology is solid and powerful enough to improve decision-making significantly. For example, predictive analytics (e.g., time series forecasting) is already a proven benefit. Such technology is smart enough to recognise irregularities and learn how a stock market will move in real-time. With the advancement of digital innovation, researchers have focused on deep learning (DL) models to get a more accurate and unbiased estimation. Consequently, this paper presents a novel DL approach for time series prediction using a combination of poly-linear regression with Long Short-Term Memory (LSTM) and data augmentation. It is consequently named Poly-linear Regression with Augmented Long Short Term Memory Neural Network (PLR-ALSTM-NN). The proposed DL model can be exploited to predict the future financial markets more accurately than existing state-of-the-art neural networks and machine learning tools. In order to make the model a more generic one, it is first validated on four financial market time-series datasets and then also implemented on a supply chain time-series dataset to predict sales data. LSTM, with its feedback connections, can process an entire series of data as well as single data points and statistical regression establishes the strength and character of the relationship between some dependent and independent variables. After doing experimental validations and based on the long-term and short-term predicted data, the suitability of the proposed PLR-ALSTM-NN is well-grounded against a few recent and advanced state-of-the-art machine learning, and DL approaches." @default.
• W4281558554 created "2022-05-27" @default.
• W4281558554 creator A5003507463 @default.
• W4281558554 creator A5054715965 @default.
• W4281558554 creator A5067173249 @default.
• W4281558554 creator A5083552227 @default.
• W4281558554 date "2022-08-01" @default.
• W4281558554 modified "2023-10-12" @default.
• W4281558554 title "Poly-linear regression with augmented long short term memory neural network: Predicting time series data" @default.
• W4281558554 cites W2079735306 @default.
• W4281558554 cites W2098063401 @default.
• W4281558554 cites W2395786628 @default.
• W4281558554 cites W2511683089 @default.
• W4281558554 cites W2523498403 @default.
• W4281558554 cites W2624385633 @default.
• W4281558554 cites W2734777338 @default.
• W4281558554 cites W2742229469 @default.
• W4281558554 cites W2802914173 @default.
• W4281558554 cites W2893960435 @default.
• W4281558554 cites W2898260509 @default.
• W4281558554 cites W2919115771 @default.
• W4281558554 cites W2981537679 @default.
• W4281558554 cites W3005486893 @default.
• W4281558554 cites W3010721861 @default.
• W4281558554 cites W3014278717 @default.
• W4281558554 cites W3015568951 @default.
• W4281558554 cites W3018306781 @default.
• W4281558554 cites W3036860367 @default.
• W4281558554 cites W3041181542 @default.
• W4281558554 cites W3110378470 @default.
• W4281558554 cites W3110827398 @default.
• W4281558554 cites W3117894835 @default.
• W4281558554 cites W3127482459 @default.
• W4281558554 cites W3156959920 @default.
• W4281558554 cites W3191689323 @default.
• W4281558554 cites W3199363485 @default.
• W4281558554 cites W3215395840 @default.
• W4281558554 cites W333233685 @default.
• W4281558554 cites W4200287510 @default.
• W4281558554 cites W4213225422 @default.
• W4281558554 cites W4213276360 @default.
• W4281558554 cites W4221036632 @default.
• W4281558554 cites W67087259 @default.
• W4281558554 doi "https://doi.org/10.1016/j.ins.2022.05.078" @default.
• W4281558554 hasPublicationYear "2022" @default.
• W4281558554 type Work @default.
• W4281558554 citedByCount "7" @default.
• W4281558554 countsByYear W42815585542022 @default.
• W4281558554 countsByYear W42815585542023 @default.
• W4281558554 crossrefType "journal-article" @default.
• W4281558554 hasAuthorship W4281558554A5003507463 @default.
• W4281558554 hasAuthorship W4281558554A5054715965 @default.
• W4281558554 hasAuthorship W4281558554A5067173249 @default.
• W4281558554 hasAuthorship W4281558554A5083552227 @default.
• W4281558554 hasConcept C105795698 @default.
• W4281558554 hasConcept C119857082 @default.
• W4281558554 hasConcept C121332964 @default.
• W4281558554 hasConcept C133488467 @default.
• W4281558554 hasConcept C143724316 @default.
• W4281558554 hasConcept C147168706 @default.
• W4281558554 hasConcept C149782125 @default.
• W4281558554 hasConcept C151406439 @default.
• W4281558554 hasConcept C151730666 @default.
• W4281558554 hasConcept C152877465 @default.
• W4281558554 hasConcept C154945302 @default.
• W4281558554 hasConcept C33923547 @default.
• W4281558554 hasConcept C41008148 @default.
• W4281558554 hasConcept C48921125 @default.
• W4281558554 hasConcept C50644808 @default.
• W4281558554 hasConcept C61797465 @default.
• W4281558554 hasConcept C62520636 @default.
• W4281558554 hasConcept C83546350 @default.
• W4281558554 hasConcept C86803240 @default.
• W4281558554 hasConceptScore W4281558554C105795698 @default.
• W4281558554 hasConceptScore W4281558554C119857082 @default.
• W4281558554 hasConceptScore W4281558554C121332964 @default.
• W4281558554 hasConceptScore W4281558554C133488467 @default.
• W4281558554 hasConceptScore W4281558554C143724316 @default.
• W4281558554 hasConceptScore W4281558554C147168706 @default.
• W4281558554 hasConceptScore W4281558554C149782125 @default.
• W4281558554 hasConceptScore W4281558554C151406439 @default.
• W4281558554 hasConceptScore W4281558554C151730666 @default.
• W4281558554 hasConceptScore W4281558554C152877465 @default.
• W4281558554 hasConceptScore W4281558554C154945302 @default.
• W4281558554 hasConceptScore W4281558554C33923547 @default.
• W4281558554 hasConceptScore W4281558554C41008148 @default.
• W4281558554 hasConceptScore W4281558554C48921125 @default.
• W4281558554 hasConceptScore W4281558554C50644808 @default.
• W4281558554 hasConceptScore W4281558554C61797465 @default.
• W4281558554 hasConceptScore W4281558554C62520636 @default.
• W4281558554 hasConceptScore W4281558554C83546350 @default.
• W4281558554 hasConceptScore W4281558554C86803240 @default.
• W4281558554 hasLocation W42815585541 @default.
• W4281558554 hasOpenAccess W4281558554 @default.
• W4281558554 hasPrimaryLocation W42815585541 @default.
• W4281558554 hasRelatedWork W1230133629 @default.
• W4281558554 hasRelatedWork W2023475031 @default.
• W4281558554 hasRelatedWork W2037021038 @default.

• next