FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3037979079> ?p ?o ?g. }

• W3037979079 endingPage "2050039" @default.
• W3037979079 startingPage "2050039" @default.
• W3037979079 abstract "Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data." @default.
• W3037979079 created "2020-07-02" @default.
• W3037979079 creator A5023082133 @default.
• W3037979079 creator A5028834456 @default.
• W3037979079 date "2020-06-26" @default.
• W3037979079 modified "2023-10-03" @default.
• W3037979079 title "Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting" @default.
• W3037979079 cites W1534798215 @default.
• W3037979079 cites W1939526929 @default.
• W3037979079 cites W1969886729 @default.
• W3037979079 cites W1983624708 @default.
• W3037979079 cites W1986476236 @default.
• W3037979079 cites W1997483489 @default.
• W3037979079 cites W2007221293 @default.
• W3037979079 cites W2013945756 @default.
• W3037979079 cites W2026869578 @default.
• W3037979079 cites W2028702910 @default.
• W3037979079 cites W2036664543 @default.
• W3037979079 cites W2050127080 @default.
• W3037979079 cites W2061062671 @default.
• W3037979079 cites W2063640194 @default.
• W3037979079 cites W2096565207 @default.
• W3037979079 cites W2098923575 @default.
• W3037979079 cites W2117014758 @default.
• W3037979079 cites W2128692565 @default.
• W3037979079 cites W2192244518 @default.
• W3037979079 cites W2461253922 @default.
• W3037979079 cites W2621512993 @default.
• W3037979079 cites W2736832651 @default.
• W3037979079 cites W2741582347 @default.
• W3037979079 cites W2750538963 @default.
• W3037979079 cites W2763120338 @default.
• W3037979079 cites W2769921776 @default.
• W3037979079 cites W2792897601 @default.
• W3037979079 cites W2887911837 @default.
• W3037979079 cites W2888735433 @default.
• W3037979079 cites W2898456550 @default.
• W3037979079 cites W2899023105 @default.
• W3037979079 cites W2899789612 @default.
• W3037979079 cites W2905749592 @default.
• W3037979079 cites W2914894615 @default.
• W3037979079 cites W2932857337 @default.
• W3037979079 cites W2973839564 @default.
• W3037979079 cites W2984918611 @default.
• W3037979079 cites W2994157850 @default.
• W3037979079 cites W3125786970 @default.
• W3037979079 doi "https://doi.org/10.1142/s0129065720500392" @default.
• W3037979079 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32588684" @default.
• W3037979079 hasPublicationYear "2020" @default.
• W3037979079 type Work @default.
• W3037979079 sameAs 3037979079 @default.
• W3037979079 citedByCount "23" @default.
• W3037979079 countsByYear W30379790792020 @default.
• W3037979079 countsByYear W30379790792021 @default.
• W3037979079 countsByYear W30379790792022 @default.
• W3037979079 countsByYear W30379790792023 @default.
• W3037979079 crossrefType "journal-article" @default.
• W3037979079 hasAuthorship W3037979079A5023082133 @default.
• W3037979079 hasAuthorship W3037979079A5028834456 @default.
• W3037979079 hasConcept C119857082 @default.
• W3037979079 hasConcept C121332964 @default.
• W3037979079 hasConcept C127413603 @default.
• W3037979079 hasConcept C13280743 @default.
• W3037979079 hasConcept C133731056 @default.
• W3037979079 hasConcept C143724316 @default.
• W3037979079 hasConcept C149782125 @default.
• W3037979079 hasConcept C151406439 @default.
• W3037979079 hasConcept C151730666 @default.
• W3037979079 hasConcept C154945302 @default.
• W3037979079 hasConcept C158622935 @default.
• W3037979079 hasConcept C159877910 @default.
• W3037979079 hasConcept C161584116 @default.
• W3037979079 hasConcept C185798385 @default.
• W3037979079 hasConcept C205649164 @default.
• W3037979079 hasConcept C31258907 @default.
• W3037979079 hasConcept C33923547 @default.
• W3037979079 hasConcept C38858127 @default.
• W3037979079 hasConcept C41008148 @default.
• W3037979079 hasConcept C42536954 @default.
• W3037979079 hasConcept C47702885 @default.
• W3037979079 hasConcept C50644808 @default.
• W3037979079 hasConcept C62520636 @default.
• W3037979079 hasConcept C75778745 @default.
• W3037979079 hasConcept C86803240 @default.
• W3037979079 hasConceptScore W3037979079C119857082 @default.
• W3037979079 hasConceptScore W3037979079C121332964 @default.
• W3037979079 hasConceptScore W3037979079C127413603 @default.
• W3037979079 hasConceptScore W3037979079C13280743 @default.
• W3037979079 hasConceptScore W3037979079C133731056 @default.
• W3037979079 hasConceptScore W3037979079C143724316 @default.
• W3037979079 hasConceptScore W3037979079C149782125 @default.
• W3037979079 hasConceptScore W3037979079C151406439 @default.
• W3037979079 hasConceptScore W3037979079C151730666 @default.
• W3037979079 hasConceptScore W3037979079C154945302 @default.
• W3037979079 hasConceptScore W3037979079C158622935 @default.
• W3037979079 hasConceptScore W3037979079C159877910 @default.
• W3037979079 hasConceptScore W3037979079C161584116 @default.
• W3037979079 hasConceptScore W3037979079C185798385 @default.

• next