Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to the inherent volatility
and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price …
and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price …
Hybrid information mixing module for stock movement prediction
J Choi, S Yoo, X Zhou, Y Kim - IEEE Access, 2023 - ieeexplore.ieee.org
With the continuing active research on deep learning, research on stock price prediction
using deep learning has been actively conducted in the financial industry. This paper …
using deep learning has been actively conducted in the financial industry. This paper …
Doubleadapt: A meta-learning approach to incremental learning for stock trend forecasting
Stock trend forecasting is a fundamental task of quantitative investment where precise
predictions of price trends are indispensable. As an online service, stock data continuously …
predictions of price trends are indispensable. As an online service, stock data continuously …
Incremental Learning of Stock Trends via Meta-Learning with Dynamic Adaptation
S Huang, Z Liu, Y Deng, Q Li - arXiv preprint arXiv:2401.03865, 2024 - arxiv.org
Forecasting the trend of stock prices is an enduring topic at the intersection of finance and
computer science. Incremental updates to forecasters have proven effective in alleviating the …
computer science. Incremental updates to forecasters have proven effective in alleviating the …
[PDF][PDF] An open and large-scale dataset for multi-modal climate change-aware crop yield predictions
Precise crop yield predictions are of national importance for ensuring food security and
sustainable agricultural practices. While AI-for-science approaches have exhibited …
sustainable agricultural practices. While AI-for-science approaches have exhibited …
Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction
Long sequence prediction has broad and significant application value in fields such as
finance, wind power, and weather. However, the complex long-term dependencies of long …
finance, wind power, and weather. However, the complex long-term dependencies of long …
Contrastive Learning Framework for Bitcoin Crash Prediction
Due to spectacular gains during periods of rapid price increase and unpredictably large
drops, Bitcoin has become a popular emergent asset class over the past few years. In this …
drops, Bitcoin has become a popular emergent asset class over the past few years. In this …
Daily Physical Activity Monitoring--Adaptive Learning from Multi-source Motion Sensor Data
In healthcare applications, there is a growing need to develop machine learning models that
use data from a single source, such as that from a wrist wearable device, to monitor physical …
use data from a single source, such as that from a wrist wearable device, to monitor physical …
Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification
Multivariate time series (MTS) data is generated through multiple sensors across various
domains such as engineering application, health monitoring, and the internet of things …
domains such as engineering application, health monitoring, and the internet of things …
Contrastive Learning Based Framework for Stock Bubbles and Crashes Prediction
Z Liu - 2023 - search.proquest.com
Stock bubbles and crashes have received much attention from economists and traders alike
for decades. Accurate forecasting of bubbles and crashes is central to the management of …
for decades. Accurate forecasting of bubbles and crashes is central to the management of …