Matches in SemOpenAlex for { <https://semopenalex.org/work/W3118809783> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W3118809783 endingPage "108" @default.
- W3118809783 startingPage "89" @default.
- W3118809783 abstract "The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy." @default.
- W3118809783 created "2021-01-18" @default.
- W3118809783 creator A5010209255 @default.
- W3118809783 creator A5022700946 @default.
- W3118809783 creator A5046594273 @default.
- W3118809783 creator A5062757579 @default.
- W3118809783 date "2020-12-01" @default.
- W3118809783 modified "2023-09-26" @default.
- W3118809783 title "Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction" @default.
- W3118809783 cites W2025623395 @default.
- W3118809783 cites W2973765623 @default.
- W3118809783 cites W2987147499 @default.
- W3118809783 cites W3011387630 @default.
- W3118809783 doi "https://doi.org/10.22059/jitm.2020.79368" @default.
- W3118809783 hasPublicationYear "2020" @default.
- W3118809783 type Work @default.
- W3118809783 sameAs 3118809783 @default.
- W3118809783 citedByCount "1" @default.
- W3118809783 countsByYear W31188097832020 @default.
- W3118809783 crossrefType "journal-article" @default.
- W3118809783 hasAuthorship W3118809783A5010209255 @default.
- W3118809783 hasAuthorship W3118809783A5022700946 @default.
- W3118809783 hasAuthorship W3118809783A5046594273 @default.
- W3118809783 hasAuthorship W3118809783A5062757579 @default.
- W3118809783 hasConcept C10138342 @default.
- W3118809783 hasConcept C119857082 @default.
- W3118809783 hasConcept C127413603 @default.
- W3118809783 hasConcept C149782125 @default.
- W3118809783 hasConcept C151730666 @default.
- W3118809783 hasConcept C154945302 @default.
- W3118809783 hasConcept C162324750 @default.
- W3118809783 hasConcept C200870193 @default.
- W3118809783 hasConcept C204036174 @default.
- W3118809783 hasConcept C2780299701 @default.
- W3118809783 hasConcept C2780762169 @default.
- W3118809783 hasConcept C41008148 @default.
- W3118809783 hasConcept C50644808 @default.
- W3118809783 hasConcept C78519656 @default.
- W3118809783 hasConcept C8642999 @default.
- W3118809783 hasConcept C86803240 @default.
- W3118809783 hasConceptScore W3118809783C10138342 @default.
- W3118809783 hasConceptScore W3118809783C119857082 @default.
- W3118809783 hasConceptScore W3118809783C127413603 @default.
- W3118809783 hasConceptScore W3118809783C149782125 @default.
- W3118809783 hasConceptScore W3118809783C151730666 @default.
- W3118809783 hasConceptScore W3118809783C154945302 @default.
- W3118809783 hasConceptScore W3118809783C162324750 @default.
- W3118809783 hasConceptScore W3118809783C200870193 @default.
- W3118809783 hasConceptScore W3118809783C204036174 @default.
- W3118809783 hasConceptScore W3118809783C2780299701 @default.
- W3118809783 hasConceptScore W3118809783C2780762169 @default.
- W3118809783 hasConceptScore W3118809783C41008148 @default.
- W3118809783 hasConceptScore W3118809783C50644808 @default.
- W3118809783 hasConceptScore W3118809783C78519656 @default.
- W3118809783 hasConceptScore W3118809783C8642999 @default.
- W3118809783 hasConceptScore W3118809783C86803240 @default.
- W3118809783 hasLocation W31188097831 @default.
- W3118809783 hasOpenAccess W3118809783 @default.
- W3118809783 hasPrimaryLocation W31188097831 @default.
- W3118809783 hasRelatedWork W2160724530 @default.
- W3118809783 hasRelatedWork W2333424759 @default.
- W3118809783 hasRelatedWork W2550012350 @default.
- W3118809783 hasRelatedWork W2607162077 @default.
- W3118809783 hasRelatedWork W2763060201 @default.
- W3118809783 hasRelatedWork W2786452155 @default.
- W3118809783 hasRelatedWork W2885525669 @default.
- W3118809783 hasRelatedWork W2987147499 @default.
- W3118809783 hasRelatedWork W2994069988 @default.
- W3118809783 hasRelatedWork W3002756429 @default.
- W3118809783 hasRelatedWork W3049045648 @default.
- W3118809783 hasRelatedWork W3110166855 @default.
- W3118809783 hasRelatedWork W3127321997 @default.
- W3118809783 hasRelatedWork W3141132414 @default.
- W3118809783 hasRelatedWork W3158221728 @default.
- W3118809783 hasRelatedWork W3163959491 @default.
- W3118809783 hasRelatedWork W3179609216 @default.
- W3118809783 hasRelatedWork W3208829801 @default.
- W3118809783 hasRelatedWork W2186098326 @default.
- W3118809783 hasRelatedWork W2549746289 @default.
- W3118809783 hasVolume "12" @default.
- W3118809783 isParatext "false" @default.
- W3118809783 isRetracted "false" @default.
- W3118809783 magId "3118809783" @default.
- W3118809783 workType "article" @default.