The fields of integrated sensing and communication (ISAC), wireless communications, time series forecasting, financial forecasting, and temporal information modeling are experiencing rapid growth and innovation. A common theme among these areas is the increasing importance of advanced technologies such as reconfigurable intelligent surfaces (RIS), machine learning, and natural language processing.

In ISAC, researchers are exploring the use of RIS to enhance sensing and communication capabilities, including indoor localization and beamforming techniques. The integration of physical-layer security (PLS) with ISAC is also being investigated to provide theoretically secure transmissions and protect against sensing spoofing and communication covertness. Notable studies have highlighted the potential threats of malicious RISs to sensing safety in ISAC vehicle networks and proposed novel threat models and security solutions.

In wireless communications, RIS technology is being developed to improve coverage, quality, and security. Quasi-static IRS designs and continuous aperture array-based systems are being explored to achieve improved area coverage and energy efficiency. RIS-assisted over-the-air computation and neural network implementations are also being investigated to enable fast and low-latency processing.

Time series forecasting is another area of significant development, with a focus on improving predictive accuracy and efficiency. Hybrid approaches, such as combining fuzzy inference systems with traditional models, are being explored to enhance forecasting reliability. Innovations in transformer-based models and frequency filtering techniques are being applied to multivariate time series forecasting, demonstrating improved performance and computational efficiency.

The integration of multimodal data, particularly textual information, is also becoming increasingly important in time series analysis. Large language models and cross-modality alignment techniques are being used to enhance the accuracy and efficiency of time series forecasting. In financial forecasting, hybrid models that combine traditional techniques with machine learning and deep learning approaches are being developed to improve the accuracy of financial predictions.

Finally, temporal information modeling is witnessing significant advancements, driven by the need to effectively capture and incorporate complex time patterns into various applications. Learnable transformation functions, time-series data augmentation methods, and temporal interaction graph representation learning are being developed to enable the seamless integration of time encoding into a wide range of tasks.

Overall, these fields are experiencing rapid innovation and growth, driven by the increasing importance of advanced technologies and the need for more accurate and efficient solutions. As research continues to advance, we can expect to see significant improvements in areas such as ISAC, wireless communications, time series forecasting, financial forecasting, and temporal information modeling.