The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time-series data with the combination of dependencies which considered as residuals.
Dec 2, 2023 · In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices.
Nov 12, 2023 · In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices.
Jun 26, 2024 · In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices.
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In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices ...
Discover how the ResNLS model is revolutionizing the prediction accuracy of stock prices through a hybrid of ResNet and LSTM architectures.
ResNLS is a robust new model designed to forecast stock prices more accurately by incorporating dependencies between adjacent stock prices.
This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from ...
A new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory ...
This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from ...