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Accurate traffic flow prediction is crucial for Intelligent Transportation Systems (ITS). Traffic patterns are influenced by both temporal dynamics and road network structure, requiring the extraction and utilization of spatial and temporal ...

Urban transportation efficiency and reliability are crucial. Most approaches use predefined or adaptive graphs to model spatio-temporal dependencies, but they struggle to make accurate predictions due to the dynamic nature of traffic conditions, ...

Long-term time series forecasting has widespread applications in multiple fields. Time series possess seasonality and trend, and existing models predict time series in either the time or frequency domain, with the former generally struggling to ...

Long-term time series forecasting has widespread applications in real life, including finance, traffic, weather and sensor data analysis. Time series possess seasonal, trend and irregular components. However, current MLP-based mixing models fall ...

Question retrieval aims to retrieve historical question-answer pairs that are semantically similar or related to newly posted questions. Existing methods rely on measuring the textual similarity between the asked question and the solved question, ...

The carbon emissions online monitoring system (CEMS) of coal-fired power plants can provide accurate and real-time carbon emissions data. Real-time prediction of carbon emissions is conducive to optimizing the refined scheduling of production ...

Currently, the uncertainty in meteorological conditions presents a challenge for accurately forecasting photovoltaic (PV) power output. Based on actual data from a PV plant, this manuscript proposes a PV power generation prediction method based on ...

Stock similarity analysis is essential for stock market prediction. However, existing measures of stock similarity often focus only on analyzing the magnitude differences of single stock feature, overlooking the impacts of multidimensionality, ...

In federated learning (FL), the success of model training largely hinges on the contributions from clients. Current FL frameworks encounter obstacles in pinpointing and compensating high-contribution clients effectively. This paper proposes a ...

Long distance pipeline plays a vital role in long distance water supply facilities, and the prediction of pipeline leakage is always a difficult research problem. Data such as water flow and pressure of pipelines have obvious spatial and temporal ...

Timely and accurate traffic flow prediction holds significant value for public commuting and urban traffic management. However, the independent temporal and spatial components in recent methods struggle to fully capture the underlying spatial-...

This paper introduces an innovative approach to recommender systems by integrating social connectivity enhancement, knowledge graph augmentation, and a temporal decay mechanism within a Graph Neural Network (GNN) architecture. Our proposed model, ...

With the integration of manufacturing and the new generation of information technology, it becomes possible to utilize quality prediction technology instead of inspection technology. This paper focuses on achieving efficient product quality ...

Proposed is an online multivariate time series forecasting algorithm based on mixed convolution to address the issue of low prediction performance of existing algorithms in real-time online forecasting tasks for data streams. Firstly, a mixed ...

Recently, many deep learning-based models have been proposed for multivariate time series forecasting (MTSF). However, these models usually fail to fully exploit two crucial features of multivariate time series: periodic characteristics and cross-...

In the field of ecology, remote sensing technology is widely used to acquire time-series data of surface changes, and time-series prediction algorithms are employed to forecast the future ecological conditions of the surface. Traditional time-...

At the 2020 United Nations General Assembly, China presented its ‘dual carbon’ plan, which aims to reach the peak of carbon emissions and achieve carbon neutrality. However, accurately estimating and forecasting carbon emissions remains a ...

Government Data (GD), crucial for fostering social and economic growth, must adhere to specific classification standards and formats to ensure public accessibility and usability. Despite its potential, GD is currently hindered by a scarcity of ...

As renewable energy usage increases, power systems become more intricate and demand fluctuations intensify. Accurate short-term load forecasting (STLF) is vital for balancing energy supply and demand. Traditional models often struggle with long ...

Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems hold immense potential for application in industry. In the coal mining, the process of querying and retrieving safety information for dispatchers often consumes ...

Coreference resolution is a crucial task in conversational systems. However, most existing studies focus on only modeling the text information. Even though a few methods attempt to utilize syntactic information, but they neglect the syntactic ...

Time series forecasting is a crucial aspect of analyzing time series data, enabling predictions about future trends. Deep learning methods, particularly the transformer model, have become popular in time series forecasting. However, most existing ...

Multivariate time series classification (MTSC) has become a crucial challenge with widespread implications in diverse fields, ranging from astronomy to medical analysis. The primary hurdle in MTSC lies in effectively integrating multi-dimensional ...

As cloud computing continues to evolve, computational power leasing has emerged as a novel web service model, providing users with access to computing resources or cloud computing capabilities. This enables users to offload their computational ...

Traffic flow prediction has important implications for multiple fields, such as urban planning, traffic management and transportation. Accurate Traffic flow prediction helps improve transportation efficiency. At the same time, getting accurate ...

Existing session-based recommendation (SBR) studies typically focus on capturing sequential dependencies using recurrent neural networks or modeling high-order relations through hypergraph convolutional networks. However, in real-world scenarios, ...