{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:U65DZNOJULCV6BQ7AT2ZDMLQSZ","short_pith_number":"pith:U65DZNOJ","canonical_record":{"source":{"id":"1905.08850","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-21T20:05:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bc2c36f79d5cdd2c208e26440be059cf7f9b79c8a650829ebd0b8d33f294f671","abstract_canon_sha256":"5b19c56ac83f44526acd5d2ee57b949fb5bb2ee9b96b6a989148646d0f0b5acb"},"schema_version":"1.0"},"canonical_sha256":"a7ba3cb5c9a2c55f061f04f591b170965c8bdace75009f36516962e6ce63f6b3","source":{"kind":"arxiv","id":"1905.08850","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.08850","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.08850v1","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.08850","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"pith_short_12","alias_value":"U65DZNOJULCV","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"U65DZNOJULCV6BQ7","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"U65DZNOJ","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:U65DZNOJULCV6BQ7AT2ZDMLQSZ","target":"record","payload":{"canonical_record":{"source":{"id":"1905.08850","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-21T20:05:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bc2c36f79d5cdd2c208e26440be059cf7f9b79c8a650829ebd0b8d33f294f671","abstract_canon_sha256":"5b19c56ac83f44526acd5d2ee57b949fb5bb2ee9b96b6a989148646d0f0b5acb"},"schema_version":"1.0"},"canonical_sha256":"a7ba3cb5c9a2c55f061f04f591b170965c8bdace75009f36516962e6ce63f6b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:25.064247Z","signature_b64":"cwHxCPwO/K3n7Qhc99HyyhRTSjF26AmdCo3WdNsjdsw7vIQSWTMQeWv3sfvrEIZLOjVT9ufLTeHGb7RPiFVvAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7ba3cb5c9a2c55f061f04f591b170965c8bdace75009f36516962e6ce63f6b3","last_reissued_at":"2026-05-17T23:45:25.063816Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:25.063816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.08850","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":"2026-05-17T23:45:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lxwoz13Ro2M1Oxd0Sa1pOl+a3p2n4roRavcO+7BxxkNA5jS9zmOzsE3fzZubK4q8R4VHB0QAK/6ugjwwcFNPBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T22:59:29.270745Z"},"content_sha256":"d01b5f61425acd2f7d66a722ea8f63e93f0cdd1e5d44d2476a13f154eb8b63e5","schema_version":"1.0","event_id":"sha256:d01b5f61425acd2f7d66a722ea8f63e93f0cdd1e5d44d2476a13f154eb8b63e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:U65DZNOJULCV6BQ7AT2ZDMLQSZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Time-Smoothed Gradients for Online Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Sergul Aydore, Tianhao Zhu","submitted_at":"2019-05-21T20:05:52Z","abstract_excerpt":"Here, we study different update rules in stochastic gradient descent (SGD) for online forecasting problems. The selection of the learning rate parameter is critical in SGD. However, it may not be feasible to tune this parameter in online learning. Therefore, it is necessary to have an update rule that is not sensitive to the selection of the learning parameter. Inspired by the local regret metric that we introduced previously, we propose to use time-smoothed gradients within SGD update. Using the public data set-- GEFCom2014, we validate that our approach yields more stable results than the ot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.08850","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":""},"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":"2026-05-17T23:45:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H3OIEDJQ0j49qsUltyZMYsxB/HCODGo8PXg/1Ii65Q/kyNV0eoypcU1aaFa/eUtWQ5eUgI/DEMspzALdR/jKCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T22:59:29.271628Z"},"content_sha256":"2bb5e30caa48d5d69dc739be63a7cb34fb4e8b7c6e2da3310619d57d06305ba0","schema_version":"1.0","event_id":"sha256:2bb5e30caa48d5d69dc739be63a7cb34fb4e8b7c6e2da3310619d57d06305ba0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/bundle.json","state_url":"https://pith.science/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/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":"2026-07-01T22:59:29Z","links":{"resolver":"https://pith.science/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ","bundle":"https://pith.science/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/bundle.json","state":"https://pith.science/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U65DZNOJULCV6BQ7AT2ZDMLQSZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:U65DZNOJULCV6BQ7AT2ZDMLQSZ","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":"5b19c56ac83f44526acd5d2ee57b949fb5bb2ee9b96b6a989148646d0f0b5acb","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-21T20:05:52Z","title_canon_sha256":"bc2c36f79d5cdd2c208e26440be059cf7f9b79c8a650829ebd0b8d33f294f671"},"schema_version":"1.0","source":{"id":"1905.08850","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.08850","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.08850v1","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.08850","created_at":"2026-05-17T23:45:25Z"},{"alias_kind":"pith_short_12","alias_value":"U65DZNOJULCV","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"U65DZNOJULCV6BQ7","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"U65DZNOJ","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:2bb5e30caa48d5d69dc739be63a7cb34fb4e8b7c6e2da3310619d57d06305ba0","target":"graph","created_at":"2026-05-17T23:45:25Z","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"},"paper":{"abstract_excerpt":"Here, we study different update rules in stochastic gradient descent (SGD) for online forecasting problems. The selection of the learning rate parameter is critical in SGD. However, it may not be feasible to tune this parameter in online learning. Therefore, it is necessary to have an update rule that is not sensitive to the selection of the learning parameter. Inspired by the local regret metric that we introduced previously, we propose to use time-smoothed gradients within SGD update. Using the public data set-- GEFCom2014, we validate that our approach yields more stable results than the ot","authors_text":"Sergul Aydore, Tianhao Zhu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-21T20:05:52Z","title":"Time-Smoothed Gradients for Online Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.08850","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:d01b5f61425acd2f7d66a722ea8f63e93f0cdd1e5d44d2476a13f154eb8b63e5","target":"record","created_at":"2026-05-17T23:45:25Z","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":"5b19c56ac83f44526acd5d2ee57b949fb5bb2ee9b96b6a989148646d0f0b5acb","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-21T20:05:52Z","title_canon_sha256":"bc2c36f79d5cdd2c208e26440be059cf7f9b79c8a650829ebd0b8d33f294f671"},"schema_version":"1.0","source":{"id":"1905.08850","kind":"arxiv","version":1}},"canonical_sha256":"a7ba3cb5c9a2c55f061f04f591b170965c8bdace75009f36516962e6ce63f6b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7ba3cb5c9a2c55f061f04f591b170965c8bdace75009f36516962e6ce63f6b3","first_computed_at":"2026-05-17T23:45:25.063816Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:25.063816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cwHxCPwO/K3n7Qhc99HyyhRTSjF26AmdCo3WdNsjdsw7vIQSWTMQeWv3sfvrEIZLOjVT9ufLTeHGb7RPiFVvAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:25.064247Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.08850","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d01b5f61425acd2f7d66a722ea8f63e93f0cdd1e5d44d2476a13f154eb8b63e5","sha256:2bb5e30caa48d5d69dc739be63a7cb34fb4e8b7c6e2da3310619d57d06305ba0"],"state_sha256":"32ec2106e4159e32d986dfd649c9de656867ab4a4de569ac6d14eb892e248e0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TE4YvW1FVS4gQAiHYSdszksFEOCyjAmupcpyRZ8hrmHjS7s9W1nmbrJEaShhD0ZFNQd1PIz6+NYuFgdi5YMwAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T22:59:29.275324Z","bundle_sha256":"beeaf2f61290e64001d1be8f1f15b981fbf5ec265ca37509ae0c1da357889f40"}}