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Machine learning advances for time series forecasting
Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - 2020
In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time … predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide …
Persistent link: https://www.econbiz.de/10012390030
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Deep learning, predictability, and optimal portfolio returns
Babiak, Mykola; Baruník, Jozef - 2020
Persistent link: https://www.econbiz.de/10012542701
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Macroeconomic and financial market analyses and predictions through deep learning
Kim, Soohyon - 2020
Persistent link: https://www.econbiz.de/10012546997
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A brief history of forecasting competitions
Hyndman, Rob J. - 2019
Persistent link: https://www.econbiz.de/10012592216
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NNPF : Neural Network Particle Filter for time series data
Peerlings, Dewi; Brakel, Jan A. van den; Baştürk, Nalan - 2024
Persistent link: https://www.econbiz.de/10014524761
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Predicting prices of S&P500 index using classical methods and recurrent neural networks
Kijewskia, Mateusz; Ślepaczuk, Robert - 2020
Persistent link: https://www.econbiz.de/10012322224
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A comparison of machine learning model validation schemes for non-stationary time series data
Schnaubelt, Matthias - 2019
Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts …
Persistent link: https://www.econbiz.de/10012129462
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Coupling high-frequency data with nonlinear models in multiple-step-ahead forecasting of energy markets' volatility
Baruník, Jozef; Křehlík, Tomáš - 2014
In the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specific estimator and model when aiming for the...
Persistent link: https://www.econbiz.de/10010429924
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Machine learning for time series forecasting - a simulation study
Fischer, Thomas; Krauss, Christopher; Treichel, Alex - 2018
We present a comprehensive simulation study to assess and compare the performance of popular machine learning … reveal that advanced machine learning models are capable of approximating the optimal forecast very closely in the base case …
Persistent link: https://www.econbiz.de/10011781716
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Wie renditeträchtig sind Finanzprognosen mit Hilfe von Künstlichen Neuronalen Netzen?
Krüger, Siegfried; Schlegel, Hannes - 2002
Persistent link: https://www.econbiz.de/10001721743