Multivariate　time　series　forecasting　plays　an　important　role　in　many　domain　applications,　such　as　air　pollution　forecasting　and　traffic　forecasting.　Modeling　the　complex　dependencies　among　time　series　is　a　key　challenging　task　in　multivariate　time　series　forecasting.　Many　previous　works　have　used　graph　structures　to　learn　inter-series　correlations,　which　have　achieved　remarkable　performance.　However,　graph　networks　can　only　capture　spatio-temporal　dependencies　between　pairs　of　nodes,　which　cannot　handle　high-order　correlations　among　time　series.　We　propose　a　Dynamic　Hypergraph　Structure　Learning　model　(DHSL)　to　solve　the　above　problems.　We　generate　dynamic　hypergraph　structures　from　time　series　data　using　the　K-Nearest　Neighbors　method.　Then　a　dynamic　hypergraph　structure　learning　module　is　used　to　optimize　the　hypergraph　structure　to　obtain　more　accurate　high-order　correlations　among　nodes.　Finally,　the　hypergraph　structures　dynamically　learned　are　used　in　the　spatio-temporal　hypergraph　neural　network.　We　conduct　experiments　on　six　real-world　datasets.　The　prediction　performance　of　our　model　surpasses　existing　graph　network-based　prediction　models.　The　experimental　results　demonstrate　the　effectiveness　and　competitiveness　of　the　DHSL　model　for　multivariate　time　series　forecasting.　IEEE