FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384207379> ?p ?o ?g. }

• W4384207379 endingPage "101918" @default.
• W4384207379 startingPage "101918" @default.
• W4384207379 abstract "Nowadays, predictive models based on a time series are widely used in many fields, from geology to healthcare and from traffic management to industrial production. One of the most important tasks in machine and deep learning is designing predictive algorithms that provide increasingly reliable and accurate forecasts from a time series. This work proposes a novel ensemble approach to producing predictions in a multivariate framework. Its main idea is to reduce data dimensionality through an encoding technique, with the aim to extract useful information via single predictive procedures and then to gather all the processed data through a combiner to give the final forecast. In our framework, the combiner is composed of a hybrid neural architecture: a Convolutional Neural Network to extract local patterns from the predictions and a Recurrent Neural Network to infer information about the temporal patterns of the time series. Furthermore, a fully connected network is adopted to merge these two components and to provide the prediction. Extensive experiments on different datasets, both public and private, related to different applications have been carried out. Comparisons of the errors with conventional methods and state-of-the-art strategies confirm both accuracy and robustness of the proposed ensemble. Finally, we also show a comparison in terms of computational time, both in the hyperparameter optimization and forecasting tasks." @default.
• W4384207379 created "2023-07-14" @default.
• W4384207379 creator A5000313985 @default.
• W4384207379 creator A5002497813 @default.
• W4384207379 creator A5016445191 @default.
• W4384207379 creator A5018712726 @default.
• W4384207379 creator A5022404875 @default.
• W4384207379 creator A5081318069 @default.
• W4384207379 creator A5086764064 @default.
• W4384207379 date "2023-12-01" @default.
• W4384207379 modified "2023-09-30" @default.
• W4384207379 title "ENCODE - Ensemble neural combination for optimal dimensionality encoding in time-series forecasting" @default.
• W4384207379 cites W1946238955 @default.
• W4384207379 cites W1983883318 @default.
• W4384207379 cites W1986528915 @default.
• W4384207379 cites W2016166233 @default.
• W4384207379 cites W2066296745 @default.
• W4384207379 cites W2079560958 @default.
• W4384207379 cites W2112796928 @default.
• W4384207379 cites W2117014758 @default.
• W4384207379 cites W2120199131 @default.
• W4384207379 cites W2313648567 @default.
• W4384207379 cites W2503218125 @default.
• W4384207379 cites W2622999711 @default.
• W4384207379 cites W2747599906 @default.
• W4384207379 cites W2779495751 @default.
• W4384207379 cites W2795891046 @default.
• W4384207379 cites W2796072231 @default.
• W4384207379 cites W2796601240 @default.
• W4384207379 cites W2801973365 @default.
• W4384207379 cites W2803140164 @default.
• W4384207379 cites W2900680033 @default.
• W4384207379 cites W2909973217 @default.
• W4384207379 cites W2956195142 @default.
• W4384207379 cites W2962752580 @default.
• W4384207379 cites W2963507686 @default.
• W4384207379 cites W2969674357 @default.
• W4384207379 cites W2980994438 @default.
• W4384207379 cites W2997116410 @default.
• W4384207379 cites W2997117061 @default.
• W4384207379 cites W3003462583 @default.
• W4384207379 cites W3008931938 @default.
• W4384207379 cites W3011157584 @default.
• W4384207379 cites W3025194661 @default.
• W4384207379 cites W3034342548 @default.
• W4384207379 cites W3094910090 @default.
• W4384207379 cites W3109073683 @default.
• W4384207379 cites W3126067751 @default.
• W4384207379 cites W3143418053 @default.
• W4384207379 cites W3171884590 @default.
• W4384207379 cites W3172883511 @default.
• W4384207379 cites W3177858041 @default.
• W4384207379 cites W4239414618 @default.
• W4384207379 doi "https://doi.org/10.1016/j.inffus.2023.101918" @default.
• W4384207379 hasPublicationYear "2023" @default.
• W4384207379 type Work @default.
• W4384207379 citedByCount "0" @default.
• W4384207379 crossrefType "journal-article" @default.
• W4384207379 hasAuthorship W4384207379A5000313985 @default.
• W4384207379 hasAuthorship W4384207379A5002497813 @default.
• W4384207379 hasAuthorship W4384207379A5016445191 @default.
• W4384207379 hasAuthorship W4384207379A5018712726 @default.
• W4384207379 hasAuthorship W4384207379A5022404875 @default.
• W4384207379 hasAuthorship W4384207379A5081318069 @default.
• W4384207379 hasAuthorship W4384207379A5086764064 @default.
• W4384207379 hasBestOaLocation W43842073791 @default.
• W4384207379 hasConcept C104317684 @default.
• W4384207379 hasConcept C10485038 @default.
• W4384207379 hasConcept C111030470 @default.
• W4384207379 hasConcept C119857082 @default.
• W4384207379 hasConcept C119898033 @default.
• W4384207379 hasConcept C12267149 @default.
• W4384207379 hasConcept C124101348 @default.
• W4384207379 hasConcept C125411270 @default.
• W4384207379 hasConcept C147168706 @default.
• W4384207379 hasConcept C151406439 @default.
• W4384207379 hasConcept C154945302 @default.
• W4384207379 hasConcept C161584116 @default.
• W4384207379 hasConcept C185592680 @default.
• W4384207379 hasConcept C197129107 @default.
• W4384207379 hasConcept C23123220 @default.
• W4384207379 hasConcept C41008148 @default.
• W4384207379 hasConcept C50644808 @default.
• W4384207379 hasConcept C55493867 @default.
• W4384207379 hasConcept C63479239 @default.
• W4384207379 hasConcept C66746571 @default.
• W4384207379 hasConcept C81363708 @default.
• W4384207379 hasConcept C8642999 @default.
• W4384207379 hasConceptScore W4384207379C104317684 @default.
• W4384207379 hasConceptScore W4384207379C10485038 @default.
• W4384207379 hasConceptScore W4384207379C111030470 @default.
• W4384207379 hasConceptScore W4384207379C119857082 @default.
• W4384207379 hasConceptScore W4384207379C119898033 @default.
• W4384207379 hasConceptScore W4384207379C12267149 @default.
• W4384207379 hasConceptScore W4384207379C124101348 @default.
• W4384207379 hasConceptScore W4384207379C125411270 @default.
• W4384207379 hasConceptScore W4384207379C147168706 @default.
• W4384207379 hasConceptScore W4384207379C151406439 @default.

• next