{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:QQLKPVF3YOGBE7AGCUVOK44FFF","short_pith_number":"pith:QQLKPVF3","canonical_record":{"source":{"id":"2002.03624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T10:04:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"61bfc0a73a103547bda329562dcc767a778558909c74838a08c0b4ea158bc5bf","abstract_canon_sha256":"a5f53de659312962ece6586bd8c5b5e98b801a9a18a5ffc80fe26b56b30b498b"},"schema_version":"1.0"},"canonical_sha256":"8416a7d4bbc38c127c06152ae573852943d8cee573cd471c0a5e7bc90b9b71fa","source":{"kind":"arxiv","id":"2002.03624","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.03624","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"arxiv_version","alias_value":"2002.03624v1","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.03624","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_12","alias_value":"QQLKPVF3YOGB","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_16","alias_value":"QQLKPVF3YOGBE7AG","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_8","alias_value":"QQLKPVF3","created_at":"2026-07-05T00:39:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:QQLKPVF3YOGBE7AGCUVOK44FFF","target":"record","payload":{"canonical_record":{"source":{"id":"2002.03624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T10:04:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"61bfc0a73a103547bda329562dcc767a778558909c74838a08c0b4ea158bc5bf","abstract_canon_sha256":"a5f53de659312962ece6586bd8c5b5e98b801a9a18a5ffc80fe26b56b30b498b"},"schema_version":"1.0"},"canonical_sha256":"8416a7d4bbc38c127c06152ae573852943d8cee573cd471c0a5e7bc90b9b71fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:39:41.477058Z","signature_b64":"i7b/rjSq7Bt7cIBDn7fWdZETJdbTbZQb+6/50kuiYtFvxVqXbbBGyo4XV4ColQvNy5WnbmM8026Rs/twUJrzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8416a7d4bbc38c127c06152ae573852943d8cee573cd471c0a5e7bc90b9b71fa","last_reissued_at":"2026-07-05T00:39:41.476655Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:39:41.476655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.03624","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":"2026-07-05T00:39:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ofG9KPtQ7BuiqIUuN3yT19gH8JQFzmKflyao26O3VmSNzCRf78kpOHOruaiRD61ewnMdMpYWJC5JCyjDkJ/FCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T21:37:09.707102Z"},"content_sha256":"f9cdb314a7f047905bb04e64e460462eeac1f6db6ddc12ea8d63ce9ac1e2963e","schema_version":"1.0","event_id":"sha256:f9cdb314a7f047905bb04e64e460462eeac1f6db6ddc12ea8d63ce9ac1e2963e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:QQLKPVF3YOGBE7AGCUVOK44FFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Autoencoder-based time series clustering with energy applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Anne De Moliner, Beno\\^it Grossin, Georges H\\'ebrail, Guillaume Germaine, Guillaume Richard","submitted_at":"2020-02-10T10:04:29Z","abstract_excerpt":"Time series clustering is a challenging task due to the specific nature of the data. Classical approaches do not perform well and need to be adapted either through a new distance measure or a data transformation. In this paper we investigate the combination of a convolutional autoencoder and a k-medoids algorithm to perfom time series clustering. The convolutional autoencoder allows to extract meaningful features and reduce the dimension of the data, leading to an improvement of the subsequent clustering. Using simulation and energy related data to validate the approach, experimental results s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.03624","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/2002.03624/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":"2026-07-05T00:39:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHhFgYVUtQSuSci5KizuxjyyJonih+iXCIo7v/MeSDOnFK8eoUfzSX6g7IGIAyXOMBDxTxhjmhzRKrfRZ1p/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T21:37:09.707591Z"},"content_sha256":"2ca50e2a9b9b8e7a60c1f245d54eeeb145d71fdcf6cbfbea9fac200f8c317578","schema_version":"1.0","event_id":"sha256:2ca50e2a9b9b8e7a60c1f245d54eeeb145d71fdcf6cbfbea9fac200f8c317578"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/bundle.json","state_url":"https://pith.science/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/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":"2026-08-06T21:37:09Z","links":{"resolver":"https://pith.science/pith/QQLKPVF3YOGBE7AGCUVOK44FFF","bundle":"https://pith.science/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/bundle.json","state":"https://pith.science/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QQLKPVF3YOGBE7AGCUVOK44FFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:QQLKPVF3YOGBE7AGCUVOK44FFF","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":"a5f53de659312962ece6586bd8c5b5e98b801a9a18a5ffc80fe26b56b30b498b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T10:04:29Z","title_canon_sha256":"61bfc0a73a103547bda329562dcc767a778558909c74838a08c0b4ea158bc5bf"},"schema_version":"1.0","source":{"id":"2002.03624","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.03624","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"arxiv_version","alias_value":"2002.03624v1","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.03624","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_12","alias_value":"QQLKPVF3YOGB","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_16","alias_value":"QQLKPVF3YOGBE7AG","created_at":"2026-07-05T00:39:41Z"},{"alias_kind":"pith_short_8","alias_value":"QQLKPVF3","created_at":"2026-07-05T00:39:41Z"}],"graph_snapshots":[{"event_id":"sha256:2ca50e2a9b9b8e7a60c1f245d54eeeb145d71fdcf6cbfbea9fac200f8c317578","target":"graph","created_at":"2026-07-05T00:39:41Z","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/2002.03624/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series clustering is a challenging task due to the specific nature of the data. Classical approaches do not perform well and need to be adapted either through a new distance measure or a data transformation. In this paper we investigate the combination of a convolutional autoencoder and a k-medoids algorithm to perfom time series clustering. The convolutional autoencoder allows to extract meaningful features and reduce the dimension of the data, leading to an improvement of the subsequent clustering. Using simulation and energy related data to validate the approach, experimental results s","authors_text":"Anne De Moliner, Beno\\^it Grossin, Georges H\\'ebrail, Guillaume Germaine, Guillaume Richard","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T10:04:29Z","title":"Autoencoder-based time series clustering with energy applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.03624","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:f9cdb314a7f047905bb04e64e460462eeac1f6db6ddc12ea8d63ce9ac1e2963e","target":"record","created_at":"2026-07-05T00:39:41Z","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":"a5f53de659312962ece6586bd8c5b5e98b801a9a18a5ffc80fe26b56b30b498b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T10:04:29Z","title_canon_sha256":"61bfc0a73a103547bda329562dcc767a778558909c74838a08c0b4ea158bc5bf"},"schema_version":"1.0","source":{"id":"2002.03624","kind":"arxiv","version":1}},"canonical_sha256":"8416a7d4bbc38c127c06152ae573852943d8cee573cd471c0a5e7bc90b9b71fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8416a7d4bbc38c127c06152ae573852943d8cee573cd471c0a5e7bc90b9b71fa","first_computed_at":"2026-07-05T00:39:41.476655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:39:41.476655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i7b/rjSq7Bt7cIBDn7fWdZETJdbTbZQb+6/50kuiYtFvxVqXbbBGyo4XV4ColQvNy5WnbmM8026Rs/twUJrzBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:39:41.477058Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.03624","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f9cdb314a7f047905bb04e64e460462eeac1f6db6ddc12ea8d63ce9ac1e2963e","sha256:2ca50e2a9b9b8e7a60c1f245d54eeeb145d71fdcf6cbfbea9fac200f8c317578"],"state_sha256":"7718fb1482464562b10784143286cce3a7dae645c32e2c1215d24de649b174d3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yx2YCm2i59as+d80c2p/GPQe6i6f72U6+xLY1aQAoPc84EsZB0X7yEjVgZLCOQv3RBb5KqcFMuYgTNOieRyxBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T21:37:09.711790Z","bundle_sha256":"2b6729220b8db7fbbbf287c81839edf65a4bd1aaee6144ac6224225ce3989d7c"}}