{"ID":2870201,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12708","arxiv_id":"2509.12708","title":"Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting","abstract":"A detailed analysis of precipitation data over Europe is presented, with a focus on interpolation and forecasting applications. A Spatio-temporal DeepKriging (STDK) framework has been implemented using the PyTorch platform to achieve these objectives. The proposed model is capable of handling spatio-temporal irregularities while generating high-resolution interpolations and multi-step forecasts. Reproducible code modules have been developed as standalone PyTorch implementations for the interpolation\\footnote[2]{Interpolation - https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git} and forecasting\\footnote[3]{Forecasting - https://github.com/pratiknag/pytorch-convlstm.git}, facilitating broader application to similar climate datasets. The effectiveness of this approach is demonstrated through extensive evaluation on daily precipitation measurements, highlighting predictive performance and robustness.","short_abstract":"A detailed analysis of precipitation data over Europe is presented, with a focus on interpolation and forecasting applications. A Spatio-temporal DeepKriging (STDK) framework has been implemented using the PyTorch platform to achieve these objectives. The proposed model is capable of handling spatio-temporal irregulari...","url_abs":"https://arxiv.org/abs/2509.12708","url_pdf":"https://arxiv.org/pdf/2509.12708v1","authors":"[\"Pratik Nag\"]","published":"2025-09-16T05:58:31Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.AP\"]","methods":"[]","has_code":false,"code_links":[{"ID":609745,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2870201,"paper_url":"https://arxiv.org/abs/2509.12708","paper_title":"Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting","repo_url":"https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":609746,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2870201,"paper_url":"https://arxiv.org/abs/2509.12708","paper_title":"Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting","repo_url":"https://github.com/pratiknag/pytorch-convlstm.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}