Home ›
Entities
› academia
› ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario
ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario
Research article (Expert Systems with Applications, 2023) · cited 40× · AI/ML
ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario
Summary
ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario is a scholarly article[1].
Key Facts
ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario. Retrieved May 24, 2026, from https://4ort.xyz/entity/odformer-spatialtemporal-transformers-for-long-sequence-origindestination-matrix-forecasting-against-cross-application-s
MLA“ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/odformer-spatialtemporal-transformers-for-long-sequence-origindestination-matrix-forecasting-against-cross-application-s.
BibTeX@misc{4ortxyz_odformer-spatialtemporal-transformers-for-long-sequence-origindestination-matrix-forecasting-against-cross-application-s_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario}}, year = {2026}, url = {https://4ort.xyz/entity/odformer-spatialtemporal-transformers-for-long-sequence-origindestination-matrix-forecasting-against-cross-application-s}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario — https://4ort.xyz/entity/odformer-spatialtemporal-transformers-for-long-sequence-origindestination-matrix-forecasting-against-cross-application-s (retrieved 2026-05-24)