Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Leveraging large language models (LLMs) has garnered increasing attention and
introduced novel perspectives in time series classification. However, existing approaches …
introduced novel perspectives in time series classification. However, existing approaches …
Revisited Large Language Model for Time Series Analysis through Modality Alignment
Large Language Models have demonstrated impressive performance in many pivotal web
applications such as sensor data analysis. However, since LLMs are not designed for time …
applications such as sensor data analysis. However, since LLMs are not designed for time …
Spatiotemporal Pre-Trained Large Language Model for Forecasting With Missing Values
Spatiotemporal data collected by sensors within an urban Internet of Things (IoT) system
inevitably contains some missing values, which significantly affects the accuracy of …
inevitably contains some missing values, which significantly affects the accuracy of …
MVCAR: Multi-View Collaborative Graph Network for Private Car Carbon Emission Prediction
As urbanization accelerates, the rise in private car usage has become a double-edged
sword, symbolizing economic growth while exacerbating urban air pollution due to …
sword, symbolizing economic growth while exacerbating urban air pollution due to …
Multi-scale fusion dynamic graph convolutional recurrent network for traffic forecasting
J **ao, W Zhang, W Weng, Y Zhou, Y Cong - Cluster Computing, 2025 - Springer
Traffic forecasting plays an essential role in urban planning and traffic management.
Nevertheless, the intricate spatio-temporal connections in traffic data make traffic prediction …
Nevertheless, the intricate spatio-temporal connections in traffic data make traffic prediction …
Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
X Zhou, W Wang, S Qu, Z Zhang… - arxiv preprint arxiv …, 2025 - arxiv.org
Time series forecasting has traditionally focused on univariate and multivariate numerical
data, often overlooking the benefits of incorporating multimodal information, particularly …
data, often overlooking the benefits of incorporating multimodal information, particularly …
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
Large Language Models (LLMs) have recently demonstrated significant potential in the field
of time series forecasting, offering impressive capabilities in handling complex temporal …
of time series forecasting, offering impressive capabilities in handling complex temporal …
Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct
influence on the power system. However, existing methods face challenges in assessing the …
influence on the power system. However, existing methods face challenges in assessing the …
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates
The steam drum water level is a critical parameter that directly impacts the safety and
efficiency of power plant operations. However, predicting the drum water level in boilers is …
efficiency of power plant operations. However, predicting the drum water level in boilers is …
Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture
SP Martinez, M Zimmermann, MS Offermann… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper presents a natural language processing (NLP) approach to the problem of
thoroughly comprehending song lyrics, with particular attention on genre classification, view …
thoroughly comprehending song lyrics, with particular attention on genre classification, view …