CALTM:A Context-Aware Long-Term Time-Series Forecasting Model  

作　　者：Canghong Jin Jiapeng Chen Shuyu Wu Hao Wu Shuoping Wang Jing Ying 

机构地区：[1]School of Computer and Computing Science,Hangzhou City University,Hangzhou,310015,China [2]School of Computer Science and Engineering,Macao University of Science and Technology,Macao,999078,China [3]College of Computer Science and Technology,Zhejiang University,Hangzhou,310058,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期873-891,共19页工程与科学中的计算机建模（英文）

基　　金：funded by the Natural Science Foundation of Zhejiang Province of China under Grant (No.LY21F020003);Zhejiang Science and Technology Plan Project (No.2021C02060);the Scientific Research Foundation of Hangzhou City University (No.X-202206).

摘　　要：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 longterm 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.

关 键 词：Traffic volume forecasting scene matching multi module fusion 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]