has been cited by the following article(s):
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[1]
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Neural networks for bitcoin price forecasting
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Advanced Engineering Days (AED),
2023 |
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[2]
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Forecasting through neural networks: Bitcoin price prediction
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Engineering Applications,
2023 |
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[3]
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Exchange rate forecasting with Artificial Intelligence
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Smart Cities and Regional Development (SCRD) …,
2023 |
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[4]
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Artificial Neural Networks for COVID-19 Time Series Forecasting
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Open Journal of Statistics,
2022 |
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[5]
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An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability
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2021 |
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[6]
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A Novel LSTM for Multivariate Time Series with Massive Missingness
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2020 |
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[7]
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Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries
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Journal of Quantitative Economics,
2019 |
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[8]
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Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model
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Journal of Quantitative Economics,
2018 |
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[9]
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Energy associated tuning method for short-term series forecasting by complete and incomplete datasets
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Journal of Artificial Intelligence and Soft Computing Research,
2017 |
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[10]
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Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya
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2016 |
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[11]
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Noisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: application to rainfall series
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2016 |
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[1]
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An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability
Information Fusion,
2021
DOI:10.1016/j.inffus.2021.03.010
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[2]
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A Novel LSTM for Multivariate Time Series with Massive Missingness
Sensors,
2020
DOI:10.3390/s20102832
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[3]
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Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model
Journal of Quantitative Economics,
2018
DOI:10.1007/s40953-018-0133-8
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[4]
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Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets
Journal of Artificial Intelligence and Soft Computing Research,
2017
DOI:10.1515/jaiscr-2017-0001
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[5]
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Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI),
2016
DOI:10.1109/LA-CCI.2016.7885702
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