A bibliometric literature review of stock price forecasting: from statistical model to deep learning approach
PH Vuong, LH Phu, TH Van Nguyen… - Science …, 2024 - journals.sagepub.com
We introduce a comprehensive analysis of several approaches used in stock price
forecasting, including statistical, machine learning, and deep learning models. The …
forecasting, including statistical, machine learning, and deep learning models. The …
Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation
Accurately and efficiently estimating the carbon market risk is paramount for ensuring
financial stability, promoting environmental sustainability, and facilitating informed decision …
financial stability, promoting environmental sustainability, and facilitating informed decision …
Federated quantum long short-term memory (fedqlstm)
Quantum federated learning (QFL) can facilitate collaborative learning across multiple
clients using quantum machine learning (QML) models, while preserving data privacy …
clients using quantum machine learning (QML) models, while preserving data privacy …
Scalable parameterized quantum circuits classifier
X Ding, Z Song, J Xu, Y Hou, T Yang, Z Shan - Scientific Reports, 2024 - nature.com
As a generalized quantum machine learning model, parameterized quantum circuits (PQC)
have been found to perform poorly in terms of classification accuracy and model scalability …
have been found to perform poorly in terms of classification accuracy and model scalability …
[HTML][HTML] Short-term photovoltaic power forecasting based on hybrid quantum gated recurrent unit
Photovoltaic power generation forecasting is crucial for energy management, smart grid
construction, and energy markets. This study proposes a hybrid quantum–classical gated …
construction, and energy markets. This study proposes a hybrid quantum–classical gated …
[HTML][HTML] Optimized quantum LSTM using modified electric Eel foraging optimization for real-world intelligence engineering systems
The integration of metaheuristics with machine learning methodologies presents significant
advantages, particularly in optimization and computational intelligence. This amalgamation …
advantages, particularly in optimization and computational intelligence. This amalgamation …
Quantum deep neural networks for time series analysis
Quantum machine learning (QML) has emerged as a promising domain offering significant
computational advantages over classical counterparts. In recent times, researchers have …
computational advantages over classical counterparts. In recent times, researchers have …
Application of Quantum Recurrent Neural Network in Low Resource Language Text Classification
Text sentiment analysis is an important task in natural language processing and has always
been a hot research topic. However, in low-resource regions such as South Asia, where …
been a hot research topic. However, in low-resource regions such as South Asia, where …
[PDF][PDF] Quantum Machine Learning in Climate Change and Sustainability: A Short
Climate change and its impact on global sustainability are critical challenges, demanding
innovative solutions that combine cutting-edge technologies and scientific insights. Quantum …
innovative solutions that combine cutting-edge technologies and scientific insights. Quantum …
Quantum Kernel-Based Long Short-term Memory
YC Hsu, TY Li, KC Chen - arxiv preprint arxiv:2411.13225, 2024 - arxiv.org
The integration of quantum computing into classical machine learning architectures has
emerged as a promising approach to enhance model efficiency and computational capacity …
emerged as a promising approach to enhance model efficiency and computational capacity …