Financial time series forecasting is a long‐standing challenge due to the high complexity, randomness, and nonstationary of data. While predictions are typically made based on historical data with multiple features, existing models often fail to effectively integrate and utilize these diverse inputs, limiting forecasting accuracy. To address this, we propose MSA‐xLSTM, a multimodal stock price prediction model incorporating a sentiment attention mechanism. This model is designed to enhance the integration of sentiment features and technical indicators through a multiscale processing approach and sentiment attention module. Specifically, sentiment index sequences are extracted from textual sources such as financial news and stock comments. For technical indicators, both basic and derived metrics are analyzed for correlation with the prediction target, and only highly relevant ones are selected. We validate the effectiveness of each model component through comparative and ablation experiments. Furthermore, backtesting experiments demonstrate the model’s practical value in real‐world trading scenarios.

With the application of multisource information in different fields, the combination of different types of information, such as numerical data and text information, has become a favourable choice for performing stock market analyses. Despite the rich information provided by multisource data, building structured relationships remains challenging. In addition, some market relationship-based analysis methods use a predefined graph structure as a stock relationship graph, which makes it impossible to sensitively aggregate attribute features, and these methods cannot dynamically update market relationships or relationship strengths. In this paper, we propose a novel dynamic attribute-driven graph attention network incorporating sentiment (AGATS) information, transaction data, and text data. Inspired by behavioural finance , we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through tensor fusion. In particular, real-time intramarket dependencies and key attribute information are captured with graph networks, enabling dynamic relationship and relationship strength updates. Experiments conducted on real datasets show that our model is capable of ourperforming previously developed methods in prediction and trading.

Incomplete multi-view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi-view data in real-world applications. However, existing methods often struggle to effectively deal with large-scale datasets, particularly those with a significant number of missing instances. To address these issues, we propose a novel method called Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View Data (HENRI). HENRI utilizes the consensus hubs of all views to identify informative anchors to handle large-scale incomplete datasets. Furthermore, it incorporates a novel sample-level fusion strategy that effectively integrates information from all views, leading to remarkable outcomes in both cluster formation and missing data reconstruction. HENRI demonstrates exceptional capability in capturing the underlying structures of the data and recovering missing information, even when faced with a significant number of instances with incomplete data in partial views. To validate its effectiveness, we conducted experiments on 6 complete datasets and 31 incomplete datasets, comparing against 11 baseline methods. The results are impressive, demonstrating the superior performance of HENRI over the state-of-the-art methods.

Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option. Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered. To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance.

Tensor robust principal component analysis (TRPCA) is an important algorithm for color image denoising by treating the whole image as a tensor and shrinking all singular values equally. In this paper, to improve the denoising performance of TRPCA, we propose a variant of TRPCA model. Specifically, we first introduce a nonconvex TRPCA (N-TRPCA) model which can shrink large singular values more and shrink small singular values less, so that the physical meanings of different singular values can be preserved. To take advantage of the structural redundancy of an image, we further group similar patches as a tensor according to nonlocal prior, and then apply the N-TRPCA model on this tensor. The denoised image can be obtained by aggregating all processed tensors. Experimental results demonstrate the superiority of the proposed denoising method beyond state-of-the-arts.