{"as_of":"2026-08-17T03:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d66a96107af3c78e05ef1f589adbdae02dbc4d178e737c6fb2457b66e489fce","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:16:40.399795Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.03571/citation-record","integrity":"/paper/1908.03571/integrity","json":"/paper/1908.03571/citation-record.json","paper":"/paper/1908.03571"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:16:40.633864Z","title":"A review on machinery di- agnostics and prognostics implementing condition-based maintenance,","venue":null,"work_id":"bb633e6e-7d27-48f7-a8cb-f6430f9a21f0","year":2006},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.338356Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:b6848b5e1be54a916ef700418183119da22e1fd0ac5f3388c2a6a6296699fbeb","observation_id":"41e2305b-8e50-4b87-8d36-f0fa0ea05969","resolution":{"observed_at":"2026-08-14T14:16:40.640042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.344837Z","title":"The analytic hierarchy process applied to maintenance strategy selection,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.344837Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:494dd054b7f23bc62ba8cb5e554bc5b543e53be851b09f3d957203669417ac78","observation_id":"b90edcde-259f-439b-8ffa-b08978f35db9","resolution":{"observed_at":"2026-08-14T14:16:40.344837Z","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":"2026-08-14T14:16:40.597684Z","title":"’neural-gas’ network for vector quantization and its application to time-series predic- tion,","venue":null,"work_id":"5aeed6b0-8780-4083-992d-26e2d2df5087","year":1993},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.350851Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:a8be4613fd4d66bf6c81aa27144bd76ed80c1ee6a9db8956adf8311efd0cb9f4","observation_id":"e858de46-451b-4300-9cc7-fdd2cb0bef31","resolution":{"observed_at":"2026-08-14T14:16:40.604520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.583161Z","title":null,"venue":null,"work_id":"4533c5cc-14dd-498a-9e19-4026421372f1","year":2002},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.356080Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:489ce1a59445c4b23e33c0dfc82acb806d77a7db69a284d842e4aa831260e4ea","observation_id":"96da1d77-b6bd-46df-8292-58d49b16004b","resolution":{"observed_at":"2026-08-14T14:16:40.588268Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.567780Z","title":"Back- propagation, weight-elimination and time series prediction,","venue":null,"work_id":"87645362-d827-46c5-a41e-47d1e42f54c4","year":1991},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.360663Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:d3fe5700d3e53797747735c96ab8f3c28b98e6bb8333067031c6f908e66e704e","observation_id":"cc4eca16-0068-4f25-bbaa-c6b2172441f8","resolution":{"observed_at":"2026-08-14T14:16:40.572678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.549156Z","title":"Time series forecasting using backpropagation neural networks,","venue":null,"work_id":"cbae612c-c137-4341-8af3-898cefd40795","year":1991},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.365742Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:d6627305fa1bb208cb880375c07ee06da776fd62dedf95285c90c2805df4a23b","observation_id":"19c7ceb0-5ab0-4449-a588-b9c2379d3ce5","resolution":{"observed_at":"2026-08-14T14:16:40.554174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.532991Z","title":"Time series prediction with recurrent neural networks trained by a hybrid pso–ea algorithm,","venue":null,"work_id":"f4b31e79-c09a-4287-8404-42fbc146b88a","year":2007},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.371089Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:da431244bc8a0e27f39c2341daa4eecf33f5e55adc2611d078fda68bab62effb","observation_id":"b9ed3239-0442-4903-8a46-c01313012d26","resolution":{"observed_at":"2026-08-14T14:16:40.537817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.517156Z","title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,","venue":null,"work_id":"968243b1-14ea-4c82-95df-fcd20a029a53","year":2015},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.375434Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:c8a1255cf2cbf9a5fa385d3fc05393ad5462a288d8e981251f2130c5058a73cb","observation_id":"734820e3-3b5e-4c43-921a-9726cad6c682","resolution":{"observed_at":"2026-08-14T14:16:40.522519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.502514Z","title":"Time series prediction using support vector machines: a survey,","venue":null,"work_id":"769d422d-adba-4378-a4a8-9227f6b5dbdb","year":2009},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.380819Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:8a2008578b3a05e6695d9c6267b9286fdafb5f0b720a490d0b96fbc4a88c1a8f","observation_id":"deb37c0c-9a74-49ae-bf76-d9399a59f58d","resolution":{"observed_at":"2026-08-14T14:16:40.507866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.479718Z","title":"Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data,","venue":null,"work_id":"bef9f76a-bc10-4450-822d-0a9dd1ca3ba7","year":2016},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.386245Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:c67daf12e88567753a1a89f5cf0800859a0c444553d0e06c35803aea21c6da8e","observation_id":"222b07cc-7a21-43fa-ba65-f59d9465c558","resolution":{"observed_at":"2026-08-14T14:16:40.487570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.455965Z","title":"Forecasting foreign exchange rates using recurrent neural networks,","venue":null,"work_id":"c7911fa8-45f1-46e7-a761-8a54a89aec11","year":1996},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.390522Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:1c87a93b2b3644ae65518c6412978e92798ff95c7f17432760e3d3468db7adde","observation_id":"403d0dce-bf28-43c6-8c9c-1d3cba956a87","resolution":{"observed_at":"2026-08-14T14:16:40.464932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:16:40.395409Z","title":"Recurrent neural networks for multivariate time series with missing values,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.395409Z"},"links":{"citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:975576d0db199ca535f3241f5f77ca14d4354bb1c25c5852408f712b76df18fa","observation_id":"081adaa3-a84e-43b3-9069-ef7b026ef7d8","resolution":{"observed_at":"2026-08-14T14:16:40.395409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T14:16:40.399795Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:16:40.399795Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.03571"},"observation_digest":"sha256:821c311370873533e82b53f2238c5818174f10f3a2f972b6466c4e4eb3618afb","observation_id":"d266875f-c06d-4949-87e7-da9d1643eb9e","resolution":{"observed_at":"2026-08-14T14:16:40.399795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.03571","last_updated":"2019-08-09T13:46:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T21:47:50.355161Z","submitted_at":"2019-08-09T13:46:48Z","title":"LSTM-based Flow Prediction"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":13},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:1908.03571."}