@Article{cmes.2023.043230,
AUTHOR = {Canghong Jin, Jiapeng Chen, Shuyu Wu, Hao Wu, Shuoping Wang, Jing Ying},
TITLE = {CALTM: A Context-Aware Long-Term Time-Series Forecasting Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {139},
YEAR = {2024},
NUMBER = {1},
PAGES = {873--891},
URL = {http://www.techscience.com/CMES/v139n1/55122},
ISSN = {1526-1506},
ABSTRACT = {Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow. Our model comprises three components: the similar scene matching module, responsible for extracting Similar Scene Features; the long-short term representation fusion module, which integrates scenario embeddings; and a simple fully connected layer at the head for making the final forecasting. Experimental results on real datasets demonstrate that our model outperforms other methods, particularly in medium and long-term forecasting scenarios.},
DOI = {10.32604/cmes.2023.043230}
}