{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:P52C3OH4YJD3MUARY3WVMCQW5Z","short_pith_number":"pith:P52C3OH4","canonical_record":{"source":{"id":"2207.13441","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T10:39:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"122df3b50c80843ae019fb4a620fcf47b5f262cd86e392fae04de31e9369f046","abstract_canon_sha256":"1a846d955e72bf0df33370c8c4dd50e19b5860b050161f2892918487308f457f"},"schema_version":"1.0"},"canonical_sha256":"7f742db8fcc247b65011c6ed560a16ee6e1eb1aa2c4956045048dbe357539e08","source":{"kind":"arxiv","id":"2207.13441","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.13441","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"arxiv_version","alias_value":"2207.13441v1","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13441","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_12","alias_value":"P52C3OH4YJD3","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_16","alias_value":"P52C3OH4YJD3MUAR","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_8","alias_value":"P52C3OH4","created_at":"2026-07-05T04:44:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:P52C3OH4YJD3MUARY3WVMCQW5Z","target":"record","payload":{"canonical_record":{"source":{"id":"2207.13441","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T10:39:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"122df3b50c80843ae019fb4a620fcf47b5f262cd86e392fae04de31e9369f046","abstract_canon_sha256":"1a846d955e72bf0df33370c8c4dd50e19b5860b050161f2892918487308f457f"},"schema_version":"1.0"},"canonical_sha256":"7f742db8fcc247b65011c6ed560a16ee6e1eb1aa2c4956045048dbe357539e08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:07.167562Z","signature_b64":"PcmANHvhPj470NuZuYGKhYulOlyWdChwk93xNZC6pCYP+gbpozbk3B3bnPm+L8EG3HHROuWz6UVAtGZletdACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f742db8fcc247b65011c6ed560a16ee6e1eb1aa2c4956045048dbe357539e08","last_reissued_at":"2026-07-05T04:44:07.167162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:07.167162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.13441","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:44:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lREotGkAIrnjX6J7+yzHLNcb8kELH+ZU+Mf3KPkUE+y6G1KyhwKfd0kajZMYsc6TNm70TXF4hdv6KtwD9x0RDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T05:37:22.892160Z"},"content_sha256":"3e06a3fff0162bb0d9a3f0dc234817437d174b9bc77716fb3e378abc54d78073","schema_version":"1.0","event_id":"sha256:3e06a3fff0162bb0d9a3f0dc234817437d174b9bc77716fb3e378abc54d78073"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:P52C3OH4YJD3MUARY3WVMCQW5Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Time Series Forecasting Models Copy the Past: How to Mitigate","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis, Nancy Xu","submitted_at":"2022-07-27T10:39:00Z","abstract_excerpt":"Time series forecasting is at the core of important application domains posing significant challenges to machine learning algorithms. Recently neural network architectures have been widely applied to the problem of time series forecasting. Most of these models are trained by minimizing a loss function that measures predictions' deviation from the real values. Typical loss functions include mean squared error (MSE) and mean absolute error (MAE). In the presence of noise and uncertainty, neural network models tend to replicate the last observed value of the time series, thus limiting their appli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13441","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.13441/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:44:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n6dbf9yZT+TY7y6OcuxOo/NBpPAL5fZo3gc403PfzDU3Z9YLb98jvjwSbzkcz0eBo9mdoKWC2j/79t9OeWe3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T05:37:22.892653Z"},"content_sha256":"14ce1aef9593ac8f639932a2e4a8230942728028c219bffe71b969d1f5ed9e47","schema_version":"1.0","event_id":"sha256:14ce1aef9593ac8f639932a2e4a8230942728028c219bffe71b969d1f5ed9e47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/bundle.json","state_url":"https://pith.science/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T05:37:22Z","links":{"resolver":"https://pith.science/pith/P52C3OH4YJD3MUARY3WVMCQW5Z","bundle":"https://pith.science/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/bundle.json","state":"https://pith.science/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P52C3OH4YJD3MUARY3WVMCQW5Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:P52C3OH4YJD3MUARY3WVMCQW5Z","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1a846d955e72bf0df33370c8c4dd50e19b5860b050161f2892918487308f457f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T10:39:00Z","title_canon_sha256":"122df3b50c80843ae019fb4a620fcf47b5f262cd86e392fae04de31e9369f046"},"schema_version":"1.0","source":{"id":"2207.13441","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.13441","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"arxiv_version","alias_value":"2207.13441v1","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13441","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_12","alias_value":"P52C3OH4YJD3","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_16","alias_value":"P52C3OH4YJD3MUAR","created_at":"2026-07-05T04:44:07Z"},{"alias_kind":"pith_short_8","alias_value":"P52C3OH4","created_at":"2026-07-05T04:44:07Z"}],"graph_snapshots":[{"event_id":"sha256:14ce1aef9593ac8f639932a2e4a8230942728028c219bffe71b969d1f5ed9e47","target":"graph","created_at":"2026-07-05T04:44:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.13441/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting is at the core of important application domains posing significant challenges to machine learning algorithms. Recently neural network architectures have been widely applied to the problem of time series forecasting. Most of these models are trained by minimizing a loss function that measures predictions' deviation from the real values. Typical loss functions include mean squared error (MSE) and mean absolute error (MAE). In the presence of noise and uncertainty, neural network models tend to replicate the last observed value of the time series, thus limiting their appli","authors_text":"Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis, Nancy Xu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T10:39:00Z","title":"Time Series Forecasting Models Copy the Past: How to Mitigate"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13441","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3e06a3fff0162bb0d9a3f0dc234817437d174b9bc77716fb3e378abc54d78073","target":"record","created_at":"2026-07-05T04:44:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1a846d955e72bf0df33370c8c4dd50e19b5860b050161f2892918487308f457f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T10:39:00Z","title_canon_sha256":"122df3b50c80843ae019fb4a620fcf47b5f262cd86e392fae04de31e9369f046"},"schema_version":"1.0","source":{"id":"2207.13441","kind":"arxiv","version":1}},"canonical_sha256":"7f742db8fcc247b65011c6ed560a16ee6e1eb1aa2c4956045048dbe357539e08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f742db8fcc247b65011c6ed560a16ee6e1eb1aa2c4956045048dbe357539e08","first_computed_at":"2026-07-05T04:44:07.167162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:44:07.167162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PcmANHvhPj470NuZuYGKhYulOlyWdChwk93xNZC6pCYP+gbpozbk3B3bnPm+L8EG3HHROuWz6UVAtGZletdACA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:44:07.167562Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.13441","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e06a3fff0162bb0d9a3f0dc234817437d174b9bc77716fb3e378abc54d78073","sha256:14ce1aef9593ac8f639932a2e4a8230942728028c219bffe71b969d1f5ed9e47"],"state_sha256":"0fbb403cc4445ae0eece3be43249005f4a5f969d8606986bb06fe4f08f2e5e8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DesTmXiWTGqJsUKTGAzyuzE7ddpp5d+APcf4Yr7PtgM8jYGaI/usADQZB1HbtteQ38mWgVu5wXOTpWJKp4LpCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T05:37:22.896721Z","bundle_sha256":"3f40330b7003d3b6632b8e7676a5ab2755045984b720b4ec45aaab6c41eff590"}}