{"as_of":"2026-08-21T07:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ece12f97b178face669f2b868c4ac56b8aa272da49f23cf5a7dcf41f4c4204f6","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:33:17.188071Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2412.12168/citation-record","integrity":"/paper/2412.12168/integrity","json":"/paper/2412.12168/citation-record.json","paper":"/paper/2412.12168"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.447399Z","title":"Clothing sale forecasting by a composite gru–prophet model with an attention mechanism,","venue":null,"work_id":"85428a65-d2de-4570-8f4a-51277a96c06a","year":2021},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.108348Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:a8ceaa42752ccc6b712b7f7eeb9fbb70d580a100b6b94e58880d5f5ed4fb1ea7","observation_id":"943c4110-8a5a-41f6-8bed-9390c8062184","resolution":{"observed_at":"2026-08-11T17:33:17.452943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.430872Z","title":"Short term traffic forecast - ing using time series methods,","venue":null,"work_id":"bc7e919f-20b7-46f7-a208-f3da92703880","year":1988},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.115208Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:f0fa1611d83b06d7d1bb8cb0704c3a395f14740c0e5e96648907fbf26cdb5c8b","observation_id":"608e5544-cb30-497b-bf23-e380edbdb664","resolution":{"observed_at":"2026-08-11T17:33:17.436160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.413981Z","title":"Arima models to predict next-day electricity prices,","venue":null,"work_id":"c6dfeb38-4266-4ec1-939b-2d5c31b3c7c3","year":2003},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.120278Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:b7723811089e3d93f343312519b3eae57096fa7ac8cee1e7d9a8f9e511f0f885","observation_id":"05b81b26-7aba-4411-bd73-090322ef6376","resolution":{"observed_at":"2026-08-11T17:33:17.419078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.397713Z","title":"Short-term forecasting ionospheric delay over ukm, malaysia, using the holt-winter method,","venue":null,"work_id":"03adcbbe-f440-4914-a0b8-f29561efb6b7","year":2013},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.125843Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:473a3e84a4992079ab0a71aff923dab822c078638f84900872d540023dd7073c","observation_id":"b4a4d3c1-89ee-41aa-82c6-3cf9f8b28c21","resolution":{"observed_at":"2026-08-11T17:33:17.402638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.131119Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.131119Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:2c9103fbebdf5548727c61f8a7192430df2832c14e84734a2c7048eb51b96346","observation_id":"c47bb8ad-720c-4d8f-a852-29c364abd96d","resolution":{"observed_at":"2026-08-11T17:33:17.131119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.3555","last_updated":"2014-12-11T06:46:53Z","snapshot_observed_at":"2026-08-20T00:41:09.240026Z","submitted_at":"2014-12-11T06:46:53Z","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.3555","snapshot_observed_at":"2026-08-11T17:33:17.135831Z","title":"Em- pirical evaluation of gated recurrent neural networks on sequence modeling,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.135831Z"},"links":{"cited_paper":"/paper/1412.3555","citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:8af75ff29c3f455a6f92ff8dcdfa0ea4c8db59360dee89c74f900661948419e6","observation_id":"b364a33d-48d6-4504-9d29-746ff5d8b15b","resolution":{"observed_at":"2026-08-11T17:33:17.135831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.02971","last_updated":"2017-08-14T10:15:06Z","snapshot_observed_at":"2026-08-14T21:07:42.976253Z","submitted_at":"2017-04-07T23:50:09Z","title":"A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.02971","snapshot_observed_at":"2026-08-11T17:33:17.141374Z","title":"A dual-stage attention-based recurrent neu- ral network for time series prediction,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.141374Z"},"links":{"cited_paper":"/paper/1704.02971","citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:b9d66c83f0dc064071af5acfe4de28d14656bb896fde3208d16cae6b027f2406","observation_id":"8900aeeb-5d5d-4b47-99c5-b47c951af6cb","resolution":{"observed_at":"2026-08-11T17:33:17.141374Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.371766Z","title":"Modeling long-and short-term temporal patterns with deep neural networks,","venue":null,"work_id":"9d9966b3-78b5-4274-b90c-7d887f610032","year":2018},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.146826Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:90d9e79ecbc3e592507f26ecf2fdc5cf99bfe6045283d622701d519df928a105","observation_id":"65a62662-1f98-472d-8749-b0ac16dfdbe8","resolution":{"observed_at":"2026-08-11T17:33:17.376613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.354798Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":"f71d474e-fd19-4fe7-8b8e-d5c2748d6145","year":2021},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.151810Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:d2f95e718b29759e53b1e6203335b85f2773c5f57ca1e0d50bf684b44dddf039","observation_id":"7f483b8d-eb5d-4f38-90f0-906cc159b42d","resolution":{"observed_at":"2026-08-11T17:33:17.359990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.157096Z","title":"Autoformer: De- composition transformers with auto-correlation for long- term series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.157096Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:c31973b9180dd98d3a31e1f5105b607e37a88bc89e97b4065828e38d55610d6a","observation_id":"3a8f0c09-bd94-4b2c-b87c-d95f500aaa25","resolution":{"observed_at":"2026-08-11T17:33:17.157096Z","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":"2026-08-11T17:33:17.163325Z","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.163325Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:2051182d73342bad18127bfed81f26f9d0f5f59b573314c32c37e041b4e82621","observation_id":"6e078ba3-7a89-442f-8814-4c65df28347d","resolution":{"observed_at":"2026-08-11T17:33:17.163325Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.317626Z","title":"Micn: Multi-scale local and global con- text modeling for long-term series forecasting,","venue":null,"work_id":"6ca2f45e-9726-4eb0-ba9f-2d39b3179e10","year":2022},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.168817Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:b69e34cc09771537c512da767c3ad302e4c2d40a62b56d67fa49d23a24b00565","observation_id":"2fd6136f-df4f-42af-a1c0-d0df076b4b3a","resolution":{"observed_at":"2026-08-11T17:33:17.322883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.01271","last_updated":"2018-04-19T14:32:38Z","snapshot_observed_at":"2026-08-13T10:37:24.864456Z","submitted_at":"2018-03-04T00:20:29Z","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.01271","snapshot_observed_at":"2026-08-11T17:33:17.174090Z","title":"An empirical evalua - tion of generic convolutional and recurrent networks for sequence modeling,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.174090Z"},"links":{"cited_paper":"/paper/1803.01271","citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:d323aec825e7a01271a51dd11cec7e8d8082e143d2d1d81cd209d31af6348d23","observation_id":"27cc45e7-9d6b-4279-a288-d7ee69c84ec0","resolution":{"observed_at":"2026-08-11T17:33:17.174090Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.301829Z","title":"Analysis and modeling of seasonal time series,","venue":null,"work_id":"74afbf07-6195-4b33-9e2d-eecf7162646b","year":1978},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.179356Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:95582e92221fddbb7ba38f7afccf14d65bacfd39506219d2455d727bb5b6c6b6","observation_id":"5ba8be53-b359-4a16-ab0f-a3c5f2a35b89","resolution":{"observed_at":"2026-08-11T17:33:17.306906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.183548Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.183548Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:420ea87a79287b26b5b1a0c7d7d50870a25756353a03b8134ce39ad6eedc9b59","observation_id":"c8464afd-5e90-4bbb-845f-c8a77fb6bf82","resolution":{"observed_at":"2026-08-11T17:33:17.183548Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.272233Z","title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,","venue":null,"work_id":"e4ee97b3-198f-4b31-aa76-a129c9aed212","year":2019},"citing_paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.188071Z"},"links":{"citing_paper":"/paper/2412.12168"},"observation_digest":"sha256:4644a363e29a2daafa582e9cda7bef0d2168400710bc14094d417208a8ddc5f9","observation_id":"fa893177-26c1-41bc-a33a-f81c0a8e0290","resolution":{"observed_at":"2026-08-11T17:33:17.279398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.12168","last_updated":"2024-12-12T01:37:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T23:57:14.735537Z","submitted_at":"2024-12-12T01:37:25Z","title":"A Decomposition Modeling Framework for Seasonal Time-Series Forecasting"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":16},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2412.12168."}