Integration Advanced Neural Networks Technologies: a new Opportunity for economic Time Series Forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10257852" target="_blank" >RIV/61989100:27510/24:10257852 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10900841" target="_blank" >https://ieeexplore.ieee.org/document/10900841</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/Informatics62280.2024.10900841" target="_blank" >10.1109/Informatics62280.2024.10900841</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Integration Advanced Neural Networks Technologies: a new Opportunity for economic Time Series Forecasting

Popis výsledku v původním jazyce

This paper presents the results of integrating transformer outputs into advanced adaptive architectures of convolutional neural networks (CNN) and describes the advantages of this approach compared to baseline models. The novelty of this work lies in the approach to creating models capable of leveraging the strengths of both CNNs and transformers, dynamically adjusting their architecture based on cross-validation results. The strengths of CNNs in handling local patterns through causal convolutions and activation layers are complemented by the transformers&apos; capabilities in processing long-term dependencies, which is crucial for forecasting financial time series that often exhibit noise and unpredictable behavior. Combining these technologies results in robust hybrid forecasting architectures that not only improve validation metrics, indicating higher accuracy in predictions but also reduce model execution time, making them highly practical for forecasting highly dynamic financial series. Additionally, the adaptive approach leads to more stable performance across multiple neural network models with identical parameters, although absolute resolution of this task requires further research.

Název v anglickém jazyce

Integration Advanced Neural Networks Technologies: a new Opportunity for economic Time Series Forecasting

Popis výsledku anglicky

This paper presents the results of integrating transformer outputs into advanced adaptive architectures of convolutional neural networks (CNN) and describes the advantages of this approach compared to baseline models. The novelty of this work lies in the approach to creating models capable of leveraging the strengths of both CNNs and transformers, dynamically adjusting their architecture based on cross-validation results. The strengths of CNNs in handling local patterns through causal convolutions and activation layers are complemented by the transformers&apos; capabilities in processing long-term dependencies, which is crucial for forecasting financial time series that often exhibit noise and unpredictable behavior. Combining these technologies results in robust hybrid forecasting architectures that not only improve validation metrics, indicating higher accuracy in predictions but also reduce model execution time, making them highly practical for forecasting highly dynamic financial series. Additionally, the adaptive approach leads to more stable performance across multiple neural network models with identical parameters, although absolute resolution of this task requires further research.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TQ12000017" target="_blank" >TQ12000017: CENTRUM VÝZKUMU PRO ODOLNOU, SMART, INOVATIVNÍ A UDRŽITELNOU SPOLEČNOST</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

INFORMATICS 2024 : 2024 IEEE 17th International Scientific Conference on Informatics : November 13-15, 2024, Poprad, Slovakia : proceedings

ISBN

979-8-3503-8769-8

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

168-172

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Poprad

Datum konání akce

13. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001483035700029