Matches in SemOpenAlex for { <https://semopenalex.org/work/W4300603293> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4300603293 abstract "Recently there has been a growing interest in applying neural network modelling from natural language processing to financial time series prediction problems in computational finance. Cryptocurrency price prediction is a challenging problem with non-stationary market price and volatility clustering. Cryp-tocurrency data tends to be non-stationary, which means that predictive information extracted using deep learning techniques on observed data can not be used with future data. Moreover, there is a very little signal in cryptocurrency data to indicate the future direction of the market. This paper proposes a sensible way to frame the prediction problem as a dynamic regression problem by defining the features in the feedforward neural networks and the target as an appropriate average of the historical data. The novelty of this paper is to use deep learning algorithms and statistical bootstrapping to obtain cryptocurrency price prediction and the corresponding prediction intervals. It is shown that neural networks are capable of modelling nonlinearity directly for nonlinear time series models. The proposed hybrid approach is evaluated using simulated and cryptocurrency data through numerical experiments. Moreover, Gaussian and boot-strap prediction intervals for the price and the volatility of the prediction errors, are also discussed in some detail." @default.
- W4300603293 created "2022-10-03" @default.
- W4300603293 creator A5005530905 @default.
- W4300603293 creator A5006176286 @default.
- W4300603293 creator A5038922150 @default.
- W4300603293 creator A5043885890 @default.
- W4300603293 creator A5073625942 @default.
- W4300603293 date "2022-06-01" @default.
- W4300603293 modified "2023-09-28" @default.
- W4300603293 title "Deep Learning Predictions for Cryptocurrencies" @default.
- W4300603293 cites W2073445562 @default.
- W4300603293 cites W2086063332 @default.
- W4300603293 cites W2137983211 @default.
- W4300603293 cites W2945232411 @default.
- W4300603293 cites W2957325811 @default.
- W4300603293 cites W2963507686 @default.
- W4300603293 cites W2971724044 @default.
- W4300603293 cites W2979356072 @default.
- W4300603293 cites W3120877831 @default.
- W4300603293 cites W3139255054 @default.
- W4300603293 cites W3200991750 @default.
- W4300603293 cites W4280497824 @default.
- W4300603293 doi "https://doi.org/10.1109/compsac54236.2022.00202" @default.
- W4300603293 hasPublicationYear "2022" @default.
- W4300603293 type Work @default.
- W4300603293 citedByCount "0" @default.
- W4300603293 crossrefType "proceedings-article" @default.
- W4300603293 hasAuthorship W4300603293A5005530905 @default.
- W4300603293 hasAuthorship W4300603293A5006176286 @default.
- W4300603293 hasAuthorship W4300603293A5038922150 @default.
- W4300603293 hasAuthorship W4300603293A5043885890 @default.
- W4300603293 hasAuthorship W4300603293A5073625942 @default.
- W4300603293 hasConcept C108583219 @default.
- W4300603293 hasConcept C119857082 @default.
- W4300603293 hasConcept C124101348 @default.
- W4300603293 hasConcept C149782125 @default.
- W4300603293 hasConcept C151406439 @default.
- W4300603293 hasConcept C154945302 @default.
- W4300603293 hasConcept C180706569 @default.
- W4300603293 hasConcept C207609745 @default.
- W4300603293 hasConcept C23922673 @default.
- W4300603293 hasConcept C2776142675 @default.
- W4300603293 hasConcept C33923547 @default.
- W4300603293 hasConcept C38652104 @default.
- W4300603293 hasConcept C41008148 @default.
- W4300603293 hasConcept C50644808 @default.
- W4300603293 hasConcept C73555534 @default.
- W4300603293 hasConcept C91602232 @default.
- W4300603293 hasConceptScore W4300603293C108583219 @default.
- W4300603293 hasConceptScore W4300603293C119857082 @default.
- W4300603293 hasConceptScore W4300603293C124101348 @default.
- W4300603293 hasConceptScore W4300603293C149782125 @default.
- W4300603293 hasConceptScore W4300603293C151406439 @default.
- W4300603293 hasConceptScore W4300603293C154945302 @default.
- W4300603293 hasConceptScore W4300603293C180706569 @default.
- W4300603293 hasConceptScore W4300603293C207609745 @default.
- W4300603293 hasConceptScore W4300603293C23922673 @default.
- W4300603293 hasConceptScore W4300603293C2776142675 @default.
- W4300603293 hasConceptScore W4300603293C33923547 @default.
- W4300603293 hasConceptScore W4300603293C38652104 @default.
- W4300603293 hasConceptScore W4300603293C41008148 @default.
- W4300603293 hasConceptScore W4300603293C50644808 @default.
- W4300603293 hasConceptScore W4300603293C73555534 @default.
- W4300603293 hasConceptScore W4300603293C91602232 @default.
- W4300603293 hasLocation W43006032931 @default.
- W4300603293 hasOpenAccess W4300603293 @default.
- W4300603293 hasPrimaryLocation W43006032931 @default.
- W4300603293 hasRelatedWork W2029171780 @default.
- W4300603293 hasRelatedWork W3122403238 @default.
- W4300603293 hasRelatedWork W3125005884 @default.
- W4300603293 hasRelatedWork W3139221666 @default.
- W4300603293 hasRelatedWork W4213225422 @default.
- W4300603293 hasRelatedWork W4220849353 @default.
- W4300603293 hasRelatedWork W4223943233 @default.
- W4300603293 hasRelatedWork W4309045103 @default.
- W4300603293 hasRelatedWork W4312200629 @default.
- W4300603293 hasRelatedWork W4312572637 @default.
- W4300603293 isParatext "false" @default.
- W4300603293 isRetracted "false" @default.
- W4300603293 workType "article" @default.