{"as_of":"2026-08-22T21:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b43b8b7a067a9497cd94bdb0007ffb3b5aeedee93109a75d86483e90dc33e503","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:40:29.408820Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.20587/citation-record","integrity":"/paper/2607.20587/integrity","json":"/paper/2607.20587/citation-record.json","paper":"/paper/2607.20587"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.256588Z","title":"Energy forecasting: A review and outlook,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.256588Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:74c148db1c4feac55f4b798bcb47e786e3bd565ad15a779ab404230ab8e93e32","observation_id":"a802f9b8-7f7a-43fa-bb86-f05a1704ebcb","resolution":{"observed_at":"2026-08-01T10:40:29.256588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.260994Z","title":"DiffLoad: Uncertainty quantification in electrical load forecasting with the diffusion model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.260994Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:18c77488533da9b72161d93b7b8403336bd7714fe18dd90e559e598ed544084d","observation_id":"98097ad8-a795-4c0d-a03b-9177750458d7","resolution":{"observed_at":"2026-08-01T10:40:29.260994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.07218","last_updated":"2026-08-04T18:41:33Z","snapshot_observed_at":"2026-08-14T19:10:11.902269Z","submitted_at":"2025-09-08T20:55:54Z","title":"Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.07218","snapshot_observed_at":"2026-08-01T10:40:29.264734Z","title":"Electricity demand and grid impacts of AI data centers: Challenges and prospects,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.264734Z"},"links":{"cited_paper":"/paper/2509.07218","citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:2bec39199b1d84295e90fbdc9b82fafdf6e5a5ab820e467951c45348ef5b9e89","observation_id":"c94b2966-5654-41f2-8429-407b4e2a4b15","resolution":{"observed_at":"2026-08-01T10:40:29.264734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.268802Z","title":"Probabilistic electric load forecasting: A tutorial review,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.268802Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:7df20c660cb4573da1183cf0aa8de1109a320a6aca71ff28eda2c6a743c6ce9d","observation_id":"347a776c-6112-4f93-bd4a-20bb87775b7e","resolution":{"observed_at":"2026-08-01T10:40:29.268802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.272640Z","title":"Day-ahead electricity price forecasting using the wavelet transform and ARIMA models,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.272640Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:955f4b080ce1dd06f639a8b11958ecdf8dcff2d9e8f0a2c2a2bea569bbd23bd4","observation_id":"6cdd2da3-d4de-4d44-ab25-c79bcee31741","resolution":{"observed_at":"2026-08-01T10:40:29.272640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.276455Z","title":"Short-term load forecasting based on a semi-parametric additive model,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.276455Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:5b7df4703ef05d6875eae38ce32a0bb4585e8d40599bf8208dd605c20cc81d0e","observation_id":"f849dae8-99be-4b76-8d5b-5d9608944bbe","resolution":{"observed_at":"2026-08-01T10:40:29.276455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.280734Z","title":"Assessment of forecasting techniques for solar power production with no exogenous inputs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.280734Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:3f03b9a1d80a313139db6225d1508de54459fbe3e55cf1d1436d6c1f4db4383f","observation_id":"0a77d0a7-61f3-4e2d-8cbc-4c3c9795f4bb","resolution":{"observed_at":"2026-08-01T10:40:29.280734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.284431Z","title":"Current methods and advances in forecasting of wind power generation,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.284431Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:2b2a9ecb4008d1513f3c8fd11479065588beeb178bbb74665f2f3cb450db38ad","observation_id":"abd5ad7e-ead0-4803-902a-2fcaeb86926c","resolution":{"observed_at":"2026-08-01T10:40:29.284431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.287902Z","title":"Short-term residential load forecasting based on LSTM recurrent neural network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.287902Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:22b4a34c4b2df26f0fed54401f78e40a8a3254e8efb18f7b6c7a0156d1367abd","observation_id":"e3222e1d-88d8-4ccd-9224-5c8b0af62392","resolution":{"observed_at":"2026-08-01T10:40:29.287902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.291465Z","title":"Attention-based neural load forecasting: A dynamic feature selection approach,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.291465Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:5528c5942d4b2cfee004ad54ba6002d8cc7f708dff0418c1c79ade739ea848f8","observation_id":"984b2886-323b-41e6-aa30-9bedddd2655f","resolution":{"observed_at":"2026-08-01T10:40:29.291465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.294876Z","title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.294876Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:b9f84e8637fc7190824838d964e482e98d5474cb0ed38566d628c5e2cb16aae9","observation_id":"58b8e46e-b6ab-49a0-81d4-b5f300c373d1","resolution":{"observed_at":"2026-08-01T10:40:29.294876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.298570Z","title":"Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.298570Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:66b64bc30900c5336f0ceb65628d9e9ba0d34dbc1cf5a63688f1171cdccd1d27","observation_id":"b735ac71-280b-4d11-9743-12fd71c450fb","resolution":{"observed_at":"2026-08-01T10:40:29.298570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.302293Z","title":"Autoformer: Decomposition transformers with Auto-Correlation for long-term series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.302293Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:66acb389adbb5da058c0f83f01265f68f9eb245d274b550fa2acf63f29db6eba","observation_id":"d8ade5f6-80af-470e-b8a2-6e882068473b","resolution":{"observed_at":"2026-08-01T10:40:29.302293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.305933Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.305933Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:753470e0d9adb69d5e1882031260fd07e914943bac329bfb747250b873f96c44","observation_id":"ce084193-d5ff-4fa4-bcb1-c4c4a52d0c9c","resolution":{"observed_at":"2026-08-01T10:40:29.305933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.309761Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.309761Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:85db52769c3c1e2b0b0478dc2072032d2a1e2c46208196c13f500b1a3f4adcd7","observation_id":"5d32e6d9-95bd-43ad-b8ee-a793143701db","resolution":{"observed_at":"2026-08-01T10:40:29.309761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.313450Z","title":"KARMA: A multilevel decomposition hybrid Mamba framework for multivariate long-term time series forecasting,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.313450Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:3ff45a985f0bc3481a92b6d26e8626b0f361d71923cd4c034a972605422995f7","observation_id":"a4ff6331-b01e-4749-81b2-3c04f7fd9e57","resolution":{"observed_at":"2026-08-01T10:40:29.313450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.317271Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.317271Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:b8d186b894e45b4039dc2851f672556c0a5aef708f7e8699138c6052d89c2856","observation_id":"7d217ce6-4e20-43c2-8edd-1c2df4ba3e5c","resolution":{"observed_at":"2026-08-01T10:40:29.317271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.321295Z","title":"An adaptive hybrid model for short term electricity price forecasting,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.321295Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:28f7cdd7c5906cd7f427582a96909783ea04fec2e672e65108a1d611eb51de4a","observation_id":"5dd0e909-6044-4bab-8807-6fa5ef72280d","resolution":{"observed_at":"2026-08-01T10:40:29.321295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.324910Z","title":"Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.324910Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:3592fe7b4b824d0e2dbed5d968934ba80c79beb0926df1d9bb4ee8cc72d78825","observation_id":"164e74f6-23e4-467f-810f-2cde94f78dc6","resolution":{"observed_at":"2026-08-01T10:40:29.324910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.328839Z","title":"Are transformers effective for time series forecasting?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.328839Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:243c0467be4e177ea0cb39628d98fd4ffbe943f29cf69c0b2e4de3ea4f9c1f0c","observation_id":"667e18f2-4524-46ed-9bed-39dfda7c666d","resolution":{"observed_at":"2026-08-01T10:40:29.328839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.332933Z","title":"TADNet: Temporal attention decomposition networks for probabilistic energy forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.332933Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:19271a1bf5da34d3b72c9d307d9d8a121e706f57c64d9c05de14c460370ba8ac","observation_id":"c0f26959-8bc2-4487-8a90-cad3341287aa","resolution":{"observed_at":"2026-08-01T10:40:29.332933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.336697Z","title":"A semi-empirical approach using gradient boost- ing and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.336697Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:685af0bdad99e60c7c87bd3b314e4b74fcf7489771e28956d9ca4ef3c8ac3497","observation_id":"867beab9-adeb-4890-b049-38fb27b12551","resolution":{"observed_at":"2026-08-01T10:40:29.336697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.340384Z","title":"Probabilistic solar power forecasting based on weather scenario generation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.340384Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:52e13f7fbef421427eae555643c98830dd97342d828a5e964cccecfa4b9f9676","observation_id":"d317100e-6984-42e2-bf19-94f90593db24","resolution":{"observed_at":"2026-08-01T10:40:29.340384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.344031Z","title":"Weather-informed probabilistic forecasting and scenario generation in power systems,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.344031Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:84d24afec9d7b2ff6080934e60ba574d0fdeb5e33c790fedc56cc82fa200f302","observation_id":"b305d1dd-942d-4e75-b3e8-1a5bb0097900","resolution":{"observed_at":"2026-08-01T10:40:29.344031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.347624Z","title":"Multi-source and temporal attention network for probabilistic wind power prediction,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.347624Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:9aa28ac562d943511f5ed20fb330bf3551ad0a96d997d5570a7b9ad27275c3a7","observation_id":"92d0af61-4272-4154-91b3-6ffa13241adf","resolution":{"observed_at":"2026-08-01T10:40:29.347624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.351015Z","title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.351015Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:30b780cb00cd7f3b6dc297998bf06df08df92962ef3fcb5f868a4493f190c098","observation_id":"56d88a1a-d3ea-46b3-bd88-58d9bc60990b","resolution":{"observed_at":"2026-08-01T10:40:29.351015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.355218Z","title":"Adaptive probabilistic forecasting of electricity (net-) load,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.355218Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:34a135b6b89f6274ff7f16c52ff9acf591be730ff898bc1b7f96fd4208fd5769","observation_id":"7a764b1f-371d-4bdb-8236-c44f2413401f","resolution":{"observed_at":"2026-08-01T10:40:29.355218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.358978Z","title":"TimeXer: Empowering transformers for time series forecasting with exogenous variables,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.358978Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:20814fa3a2701521e35149120704d40fb7e9a35f6c7689c6b35a7e20ca7635a3","observation_id":"2991b162-7aa0-48cd-9f56-e56f1f3b3b79","resolution":{"observed_at":"2026-08-01T10:40:29.358978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.362995Z","title":"Quantile regression based probabilistic forecasting of renewable energy generation and building electrical load: A state of the art review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.362995Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:b8818b36c88cd7732fa837b18302b3f02f2e5fe14676b0d66a18d50c01e45211","observation_id":"4004a43d-d413-4598-9eee-4398bde6b97e","resolution":{"observed_at":"2026-08-01T10:40:29.362995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.366630Z","title":"CRPS learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.366630Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:56dca01f081ee9948e748cc48d1576ac3e57582153e2da2b6b5eb62d4dd25485","observation_id":"78a13398-b2e0-4a0b-b473-9d98fa185360","resolution":{"observed_at":"2026-08-01T10:40:29.366630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.370586Z","title":"A new approach to linear filtering and prediction problems,","venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.370586Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:006df4a55a4c71d2853849fe1b4494bfbae466e0491f45031ffdfd15684b9829","observation_id":"bd928333-3a27-4c5c-b5f3-7b5b5a9e8ecf","resolution":{"observed_at":"2026-08-01T10:40:29.370586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-01T10:40:29.374417Z","title":"Efficiently modeling long sequences with structured state spaces,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.374417Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:861873b30688b940ab43b536c46b84367b6a66343ac4b01ea9caa31f369f4c79","observation_id":"a4ff507a-cada-4f28-a781-1f20a03c2b78","resolution":{"observed_at":"2026-08-01T10:40:29.374417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.378521Z","title":"Reversible instance normalization for accurate time-series forecasting against distribution shift,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.378521Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:935eacfd69c5f0fe61db658469c61a416cd587941434b5fe27c408d995784b9c","observation_id":"24ee0fbe-d83f-4d2f-8999-96707e89bf84","resolution":{"observed_at":"2026-08-01T10:40:29.378521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.382276Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.382276Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:33e2654be70bd10fcac8bdd3241cf8d569f7ada89a2cfc35a796fa728ca80f92","observation_id":"db153404-ed57-4477-ac6c-f1270f9ebdd6","resolution":{"observed_at":"2026-08-01T10:40:29.382276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.385982Z","title":"A theory for multiresolution signal decomposition: the wavelet representation,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.385982Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:c8f2b53dd87b2f255b23e547a919bd77f27e388ba6aa07ea2b5e38d989605ace","observation_id":"36ee773a-b37b-4372-82f6-5f6bf6a15684","resolution":{"observed_at":"2026-08-01T10:40:29.385982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.389508Z","title":"Root mean square layer normalization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.389508Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:3b31d26e5b36651b9d9a15856671060208978011534c737d51236d559e532608","observation_id":"53430ffe-6c79-43cc-b663-5e2462da02d8","resolution":{"observed_at":"2026-08-01T10:40:29.389508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.393424Z","title":"FreDF: Learning to forecast in the frequency domain,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.393424Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:e9d3ef151c38732dfb78e91c94217d7238f387fd732b9be055d3fd3c0d9c3575","observation_id":"fdd8eb31-8742-4bdf-803e-90c802a70224","resolution":{"observed_at":"2026-08-01T10:40:29.393424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.396955Z","title":"ElectricityLoadDiagrams20112014,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.396955Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:7fe698eee7a54ccb5a1ac8a9c4d65055c0a0df695dfc31192c7631ffebc18621","observation_id":"ef64e309-a269-4a96-943a-193af54582be","resolution":{"observed_at":"2026-08-01T10:40:29.396955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.401108Z","title":"Open power system data–frictionless data for electricity system modelling,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.401108Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:75794cd6685d62bd1fc202dd3da03884ddc0c286411da1097229f246eba5d268","observation_id":"1363b09a-9c24-4d43-be60-ba54e17667ca","resolution":{"observed_at":"2026-08-01T10:40:29.401108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.404994Z","title":"Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.404994Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:802929bcc04ed81c91e957c0ba8da554508d65a58c41754c87eb7cc57cbc53de","observation_id":"f257c18e-eddd-46f4-a687-c628f1e84102","resolution":{"observed_at":"2026-08-01T10:40:29.404994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:40:29.408820Z","title":"Long-term forecasting with TiDE: Time-series dense encoder,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T10:40:29.408820Z"},"links":{"citing_paper":"/paper/2607.20587"},"observation_digest":"sha256:c64d86629b4076e5c2b26cec078efe7edb678c1384fac1b8b6b39675ae5c5ee8","observation_id":"f84cd8d4-b232-4efa-b2a1-5a69adee39fb","resolution":{"observed_at":"2026-08-01T10:40:29.408820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20587","last_updated":"2026-07-22T13:58:17Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-15T05:39:08.825425Z","submitted_at":"2026-07-22T13:58:17Z","title":"SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":41},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.20587."}