We investigate accuracy, neural network complexity and sample size problem in multilayer perceptron (MLP) based (neuro-linear) feature extraction.
Sep 6, 2005 · We investigate accuracy, neural network complexity and sample size problem in multilayer perceptron (MLP) based (neuro-linear) feature ...
We investigate accuracy, neural network complexity and sample size problem in multilayer perceptron (MLP) based (neuro-linear) feature extraction.
The MTGNN model attained an impressive 92% accuracy, demonstrating its efficacy in the spatiotemporal area of power transformer problem detection. In the ...
This study proposes a two-stage model that optimally selects driving predictors for crude oil price forecasting by integrating Granger causality test (GCT) and ...
Predicting WTI Crude Oil Returns Using Machine Learning
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Feb 8, 2024 · This study explores the use of machine learning algorithms to predict the direction of daily West Texas Intermediate (WTI) crude oil prices.
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Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting Task. Conference Paper. Sep 2005; Lect Notes Comput Sci. Aistis Raudys. We ...
The results demonstrate that the LSTM model regularly surpasses the MLP model in the three benchmarks. In particular, the LSTM model demonstrates lower values ...
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Jul 16, 2024 · In this study, we implemented 16 deep- and machine-learning models to forecast the daily price of the West Texas Intermediate (WTI), Brent, gold, and silver ...
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May 20, 2024 · This study delves into an innovative research framework aimed at enhancing the precision of crude oil return rate predictions.