DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction
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- Poufinas, Thomas, 2008. "On the number of deviations of Geometric Brownian Motion with drift from its extreme points with applications to transaction costs," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3040-3046, December.
- Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004.
"Forecasting economic and financial time-series with non-linear models,"
International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
- Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
- Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006.
"Exponential smoothing model selection for forecasting,"
International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
- Baki Billah & Maxwell L King & Ralph D Snyder & Anne B Koehler, 2005. "Exponential Smoothing Model Selection for Forecasting," Monash Econometrics and Business Statistics Working Papers 6/05, Monash University, Department of Econometrics and Business Statistics.
- Shuai Sang & Lu Li, 2024. "A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
- Darko B. Vuković & Sonja D. Radenković & Ivana Simeunović & Vyacheslav Zinovev & Milan Radovanović, 2024. "Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-26, September.
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Keywords
deep operator networks; financial time series prediction; LSTM; neural networks; stock price prediction; transformers;All these keywords.
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