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SegTSF:Hierarchical Segment Learning For Lightweight Multivariate Time-Series ForeCasting 认领 引用
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作者 Hyunjun Park Hee-Gook Jun +1 位作者 Seongyong Kim Dong-Hyuk Im 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1051-1067,共17页
Time-series forecasting can significantly aid decision-making in fields in which immediate action is required,such as power demand forecasting,financial market analysis,and traffic flow management.Transformer-based mo... Time-series forecasting can significantly aid decision-making in fields in which immediate action is required,such as power demand forecasting,financial market analysis,and traffic flow management.Transformer-based models achieve high forecasting accuracy by learning complex temporal patterns;however,their extensive parameters and substantial computational costs make practical deployment difficult in latency-sensitive environments.Therefore,lightweight models based on linear layers have recently been studied for improved efficiency.However,existing linearbased models have difficulty capturing local patterns and fail to reflect sudden volatility or fine-grained local trends,limiting their overall representational capacity.In this paper,SegTSF is proposed,a linear-layer-based lightweight model for multivariate time-series forecasting that improves forecasting performance by enhancing the representational capacity of linear layers while maintaining computational efficiency.First,SegTSF reconstructs the input time series into several subsequences in units of periods and explicitly models intra-period relationships with a linear layer to capture detailed temporal information.Second,it divides each subsequence into segment units and applies individual linear layers to the relationships within and between segments to capture local patterns and global trends.Third,each predicted subsequence is reconstructed into its original dimensions to complete the final forecast.Experimental results on benchmark datasets show that the proposed SegTSF achieves performance improvement compared with existing lightweight models while using fewer parameters in various environments.The findings of this study show that SegTSF achieves a balance between efficiency and forecasting performance through hierarchical segment-wise learning within a lightweight architecture. 展开更多
关键词 Hierarchical learning lightweight model segment-based learning multivariate time-series forecasting
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Matching authority and VGI road networks using an extended node-based matching algorithm 认领 引用 被引量：4
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作者 Ehsan ABDOLMAJIDI Ali MANSOURIAN +1 位作者 Julian WILL Lars HARRIE 《Geo-Spatial Information Science》 EI CSCD 2015年第2期65-80,共16页
The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the roa... The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the road network in the VGI data set OpenStreetMap(OSM).The evaluation is based on an accurate and efficient network-matching algorithm.The study begins with a comparison of the two main strategies for network matching:segment-based and nodebased matching.The comparison shows that the result quality is comparable for the two strategies,but the node-based result is considerably more computationally efficient.Therefore,we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components.Finally,we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database(NVDB)in Scania,Sweden.The case study reveals that OSM has a completeness of 87%in the urban areas and 69%in the rural areas of Scania.The accuracy of the matching process is approximately 95%.The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions. 展开更多
关键词 geographic data volunteered geographic information(VGI) OpenStreetMap(OSM) node-based matching segment-based matching pattern detection Swedish National Road Database(NVDB)
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