Overcoming the Challenges of Uncertainty in Forecasting Economic Time Series Through Convolutional Neural Networks and Other Intelligent Approaches
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F23%3A10253257" target="_blank" >RIV/61989100:27510/23:10253257 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-39777-6_61" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-39777-6_61</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-39777-6_61" target="_blank" >10.1007/978-3-031-39777-6_61</a>
Alternative languages
Result language
angličtina
Original language name
Overcoming the Challenges of Uncertainty in Forecasting Economic Time Series Through Convolutional Neural Networks and Other Intelligent Approaches
Original language description
This article provides insights into the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs) as the tools for forecasting economic time series, where uncertainty refers to incomplete information about the future. To improve the forecasting ability of CNN architectures and capture long-term dependencies in the input sequence we used the WaveNet models which dilate convolutions with skip connections in the input sequence. The residual blocks with skip connections are defined in a specific way that allows for easier information flow through the network while avoiding the vanishing gradient problem, making it a potential innovation in the field of deep learning. Another innovative aspect is the use one-hot encoding for the target sequences using categorical cross-entropy loss function.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems. Volume 759
ISBN
978-3-031-39776-9
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
8
Pages from-to
515-522
Publisher name
Springer
Place of publication
Cham
Event location
Istanbul
Event date
Aug 22, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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