{ "schema_version": "1.0", "title": "Time-series similarity: a verified reading map", "checked_on": "2026-10-29", "record_count": 30, "scope": { "checked_on": "2026-10-29", "review_type": "Targeted narrative evidence collection; not an exhaustive systematic review.", "selection_criteria": [ "Original method papers for definitions used by the existing EchoTime atlas.", "Original comparative studies that separate preprocessing, channel assumptions or implementation cost.", "Recent 2023-2026 work directly relevant to alignment, retrieval, representations or distance software.", "Accessible publisher, conference, author-hosted paper or author-institution evidence." ], "search_services": [ "Web search using two search engines; targeted title and author queries.", "Direct publisher, conference, arXiv and author-hosted source reads." ], "representative_queries": [ "time series similarity distance survey dynamic time warping 2024 2025 benchmark", "time series foundation models similarity retrieval 2025 2026", "The Move-Split-Merge Metric for Time Series Stefan", "On The Marriage of Lp-norms and Edit Distance vldb.org", "Discovering similar multidimensional trajectories", "Robust and Fast Similarity Search for Moving Object Trajectories Chen Ozsu Oria 2005", "Soft-DTW: a Differentiable Loss Function for Time-Series", "Differentiable Divergences Between Time Series", "Matrix Profile I All Pairs Similarity Joins for Time Series", "A Structured Study of Multivariate Time-Series Distance Measures 2025 pdf", "Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping", "Fast and Exact Similarity Search in less than a Blink of an Eye", "SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling", "MOMENT: A Family of Open Time-series Foundation Models", "tsdistances Azzari", "Time Warp Edit Distance t_0", "Divergence measures based on the Shannon entropy Lin 1991" ], "limits": [ "This is a curated source set, not complete coverage of every method, paper or application.", "Per-record verification levels distinguish full-text checks, abstracts and metadata; 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the alignment restrictions are part of the method specification.", "evidence_boundary": "A DTW score depends on local cost, step pattern, endpoint policy and path restrictions. The paper does not establish that every implementation or unconstrained alignment is appropriate for every domain.", "verification_level": "Publisher metadata and abstract; original paper text.", "primary_url": "https://ieeexplore.ieee.org/abstract/document/1163055/authors", "verified_source_urls": [ "https://ieeexplore.ieee.org/abstract/document/1163055/authors", "https://jeffe.cs.illinois.edu/teaching/compgeom/refs/Sakoe-Chiba-DTW.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "26", "number": "1", "pages": "43--49" }, "doi": "10.1109/TASSP.1978.1163055" }, { "id": "vlachos2002lcss", "title": "Discovering Similar Multidimensional Trajectories", "authors": [ "Michail Vlachos", "Dimitrios Gunopulos", "George Kollios" ], "year": 2002, "venue": "Proceedings of the 18th International Conference on Data Engineering", "publication_status": "published", "source_type": "original_research", "family": "thresholded elastic matching", "citation_role": "Definition", "supported_claim": "LCSS-based trajectory matching rewards temporally ordered matching portions while allowing unmatched observations.", "evidence_boundary": "The spatial matching threshold, temporal tolerance and score normalization determine which observations count as matches. 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Ng" ], "year": 2004, "venue": "Proceedings of the 30th International Conference on Very Large Data Bases", "publication_status": "published", "source_type": "original_research", "family": "edit-based elastic matching", "citation_role": "Definition", "supported_claim": "ERP combines edit operations with numerical penalties and develops lower bounds and pruning strategies for time-series retrieval.", "evidence_boundary": "The gap reference value and local norm must be specified. Metric statements must respect the paper's domain and gap convention; arbitrary normalization, squared costs or additional constraints need separate checks.", "verification_level": "Official VLDB paper; author institution metadata.", "primary_url": "https://www.vldb.org/conf/2004/RS21P2.PDF", "verified_source_urls": [ "https://www.vldb.org/conf/2004/RS21P2.PDF", "https://researchportal.hkust.edu.hk/en/publications/on-the-marriage-of-lp-norms-and-edit-distance/" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "pages": "792--803" }, "doi": "10.1016/B978-012088469-8.50070-X" }, { "id": "chen2005edr", "title": "Robust and Fast Similarity Search for Moving Object Trajectories", "authors": [ "Lei Chen", "M. 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The paper defines strictly increasing sample timestamps and distinguishes positive temporal stiffness from its zero boundary.", "evidence_boundary": "The metric statement on value-time sequences requires positive stiffness and the stated cost assumptions. Timestamp units, a shared origin/sentinel and boundary initialization must be documented; a zero-stiffness variant does not retain the same guarantee.", "verification_level": "Author preprint full-text sections III.A, IV.C-D and Figure 2; journal metadata.", "primary_url": "https://arxiv.org/abs/cs/0703033", "verified_source_urls": [ "https://arxiv.org/abs/cs/0703033", "https://arxiv.org/pdf/cs/0703033", "https://pubmed.ncbi.nlm.nih.gov/19110495/" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "31", "number": "2", "pages": "306--318" }, "doi": "10.1109/TPAMI.2008.76", "version_or_access_note": "The arXiv record identifies the 2009 journal version. Figure 2 uses zero-valued, zero-time sentinels; the mathematical definition uses time distances." }, { "id": "stefan2013msm", "title": "The Move-Split-Merge Metric for Time Series", "authors": [ "Alexandra Stefan", "Vassilis Athitsos", "Gautam Das" ], "year": 2013, "venue": "IEEE Transactions on Knowledge and Data Engineering", "publication_status": "published", "source_type": "original_research", "family": "edit-based elastic matching", "citation_role": "Definition", "supported_claim": "MSM minimizes the cost of move, split and merge operations and supplies a quadratic-time dynamic program for the original formulation.", "evidence_boundary": "The split/merge cost is a modeling parameter. Metric guarantees belong to the specified cost scheme and parameter domain; they do not transfer automatically to multivariate extensions or transformed similarities.", "verification_level": "Author-hosted original preprint; final journal metadata corroborated.", "primary_url": "https://ranger.uta.edu/~alex/publications/stefan_tkde2012_preprint.pdf", "verified_source_urls": [ "https://ranger.uta.edu/~alex/publications/stefan_tkde2012_preprint.pdf", "https://www.scienceopen.com/document?vid=849b99f5-8fd5-4d95-ad30-245090ec583f" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "25", "number": "6", "pages": "1425--1438" }, "doi": "10.1109/TKDE.2012.88", "version_or_access_note": "The DOI and preprint carry 2012; the journal issue is June 2013. Publisher full text was not accessible in this check." }, { "id": "paparrizos2015kshape", "title": "k-Shape: Efficient and Accurate Clustering of Time Series", "authors": [ "John Paparrizos", "Luis Gravano" ], "year": 2015, "venue": "Proceedings of the ACM SIGMOD International Conference on Management of Data", "publication_status": "published", "source_type": "original_research", "family": "shift-aware shape comparison", "citation_role": "Definition", "supported_claim": "k-Shape combines a normalized cross-correlation-based shape dissimilarity with a centroid update designed for that comparison.", "evidence_boundary": "Global shifts and local time warps express different assumptions. Normalization, overlap and padding conventions matter; implementing SBD alone does not implement the complete k-Shape clustering procedure.", "verification_level": "Author-hosted original conference paper.", "primary_url": "https://www.cs.columbia.edu/~gravano/Papers/2015/sigmod2015.pdf", "verified_source_urls": [ "https://www.cs.columbia.edu/~gravano/Papers/2015/sigmod2015.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": {}, "doi": "10.1145/2723372.2737793" }, { "id": "zhao2018shapedtw", "title": "shapeDTW: Shape Dynamic Time Warping", "authors": [ "Jiaping Zhao", "Laurent Itti" ], "year": 2018, "venue": "Pattern Recognition", "publication_status": "published", "source_type": "original_research", "family": "local-descriptor elastic matching", "citation_role": "Definition", "supported_claim": "shapeDTW first describes local neighborhoods around timestamps, then aligns the resulting descriptor sequences with DTW.", "evidence_boundary": "Descriptor design and neighborhood width determine the compared structure. Reported audio-alignment and classification results do not validate a different implementation or establish universal downstream superiority.", "verification_level": "Publisher metadata, abstract and method description.", "primary_url": "https://www.sciencedirect.com/science/article/pii/S0031320317303710", "verified_source_urls": [ "https://www.sciencedirect.com/science/article/pii/S0031320317303710" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "74", "pages": "171--184" }, "doi": "10.1016/j.patcog.2017.09.020", "version_or_access_note": "Online publication was in 2017; the cited journal issue is February 2018." }, { "id": "cuturi2011gak", "title": "Fast Global Alignment Kernels", "authors": [ "Marco Cuturi" ], "year": 2011, "venue": "Proceedings of the 28th International Conference on Machine Learning", "publication_status": "published", "source_type": "original_research", "family": "alignment kernels", "citation_role": "Definition", "supported_claim": "Global alignment kernels aggregate contributions across alignments and study sufficient conditions for positive definiteness, with faster triangular variants.", "evidence_boundary": "A kernel is not automatically a distance. Exponentiating an arbitrary DTW score does not inherit positive-definiteness guarantees from this paper.", "verification_level": "Official conference paper text.", "primary_url": "https://icml.cc/2011/papers/489_icmlpaper.pdf", "verified_source_urls": [ "https://icml.cc/2011/papers/489_icmlpaper.pdf", "https://researchportal.ip-paris.fr/en/publications/fast-global-alignment-kernels/" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "pages": "929--936" } }, { "id": "cuturi2017softdtw", "title": "Soft-DTW: a Differentiable Loss Function for Time-Series", "authors": [ "Marco Cuturi", "Mathieu Blondel" ], "year": 2017, "venue": "Proceedings of the 34th International Conference on Machine Learning", "publication_status": "published", "source_type": "original_research", "family": "differentiable alignment loss", "citation_role": "Definition", "supported_claim": "Soft-DTW replaces the hard minimum over alignment costs with a smooth minimum and permits gradient-based optimization of time-series outputs.", "evidence_boundary": "The smoothing parameter changes the objective. Raw soft-DTW values are not a zero-diagonal metric, and the method should not be described as merely a faster exact DTW implementation.", "verification_level": "Official PMLR publication and original paper.", "primary_url": "https://proceedings.mlr.press/v70/cuturi17a.html", "verified_source_urls": [ "https://proceedings.mlr.press/v70/cuturi17a.html", "https://proceedings.mlr.press/v70/cuturi17a/cuturi17a.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "70", "pages": "894--903" } }, { "id": "blondel2021divergences", "title": "Differentiable Divergences Between Time Series", "authors": [ "Mathieu Blondel", "Arthur Mensch", "Jean-Philippe Vert" ], "year": 2021, "venue": "Proceedings of the 24th International Conference on Artificial Intelligence and Statistics", "publication_status": "published", "source_type": "original_research", "family": "corrected differentiable divergence", "citation_role": "Boundary", "supported_claim": "The paper identifies the entropic bias of raw soft-DTW and constructs corrected divergences, with non-negativity and equality properties under stated ground-cost conditions.", "evidence_boundary": "Non-negativity is conditional on the ground cost. A valid divergence need not obey the triangle inequality; the result does not turn every soft-DTW variant into a metric.", "verification_level": "Official PMLR publication; author research page.", "primary_url": "https://proceedings.mlr.press/v130/blondel21a.html", "verified_source_urls": [ "https://proceedings.mlr.press/v130/blondel21a.html", "https://research.google/pubs/differentiable-divergences-between-time-series/" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "130", "pages": "3853--3861" } }, { "id": "leguen2019dilate", "title": "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models", "authors": [ "Vincent Le Guen", "Nicolas Thome" ], "year": 2019, "venue": "Advances in Neural Information Processing Systems", "publication_status": "published", "source_type": "original_research", "family": "shape-and-timing forecasting loss", "citation_role": "Definition", "supported_claim": "DILATE separates shape discrepancy and temporal distortion within a differentiable objective for multi-step forecasting.", "evidence_boundary": "This is a training-objective paper. Its forecasting experiments do not establish a general-purpose retrieval ranking, calibrated similarity probability or causal relation between signals.", "verification_level": "Official NeurIPS publication and abstract.", "primary_url": "https://papers.nips.cc/paper_files/paper/2019/hash/466accbac9a66b805ba50e42ad715740-Abstract.html", "verified_source_urls": [ "https://papers.nips.cc/paper_files/paper/2019/hash/466accbac9a66b805ba50e42ad715740-Abstract.html" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "32" } }, { "id": "yeh2016matrixprofile", "title": "Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets", "authors": [ "Chin-Chia Michael Yeh", "Yan Zhu", "Liudmila Ulanova", "Nurjahan Begum", "Yifei Ding", "Hoang Anh Dau", "Diego Furtado Silva", "Abdullah Mueen", "Eamonn Keogh" ], "year": 2016, "venue": "2016 IEEE 16th International Conference on Data Mining", "publication_status": "published", "source_type": "original_research", "family": "subsequence similarity joins", "citation_role": "Definition", "supported_claim": "A matrix profile records a nearest-neighbor distance for each fixed-length subsequence; the original method builds on z-normalized Euclidean subsequence comparison.", "evidence_boundary": "A profile is an output of a similarity join, not a single whole-series distance. Window length, self-match exclusion, constants and missing values need explicit policies.", "verification_level": "Publisher abstract and original paper text.", "primary_url": "https://ieeexplore.ieee.org/document/7837992/", "verified_source_urls": [ "https://ieeexplore.ieee.org/document/7837992/", "https://www.cs.uoi.gr/~pvassil/supervision/diplomatikes/extraMaterial/Timeseries/2016_matrix_profile.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "pages": "1317--1322" }, "doi": "10.1109/ICDM.2016.0179" }, { "id": "schaefer2012sfa", "title": "SFA: A Symbolic Fourier Approximation and Index for Similarity Search in High Dimensional Datasets", "authors": [ "Patrick Schäfer", "Mikael Högqvist" ], "year": 2012, "venue": "Proceedings of the 15th International Conference on Extending Database Technology", "publication_status": "published", "source_type": "original_research", "family": "lower-bound representation and indexing", "citation_role": "Definition", "supported_claim": "SFA quantizes Fourier-based representations and combines them with a trie for exact similarity-search candidate filtering and refinement.", "evidence_boundary": "A lower-bound representation accelerates search for a specified target distance. Its symbolic comparison should not be assumed to preserve every semantic relation or arbitrary distance.", "verification_level": "Publisher abstract and author-hosted original paper.", "primary_url": "https://doi.org/10.1145/2247596.2247656", "verified_source_urls": [ "https://doi.org/10.1145/2247596.2247656", "https://www2.informatik.hu-berlin.de/~schaefpa/sfa_trie.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "pages": "516--527" }, "doi": "10.1145/2247596.2247656" }, { "id": "batista2011cid", "title": "A Complexity-Invariant Distance Measure for Time Series", "authors": [ "Gustavo E. A. P. A. Batista", "Xiaoyue Wang", "Eamonn J. Keogh" ], "year": 2011, "venue": "Proceedings of the 2011 SIAM International Conference on Data Mining", "publication_status": "published", "source_type": "original_research", "family": "complexity-adjusted distance", "citation_role": "Definition", "supported_claim": "CID adjusts a base distance using a relative series-complexity factor, motivated by the effect of differing complexities on nearest-neighbor classification.", "evidence_boundary": "The complexity estimator and its zero-complexity convention matter. The name does not imply exact invariance to every operation that changes complexity.", "verification_level": "Official SIAM publication and abstract; software definition corroborates the formula.", "primary_url": "https://epubs.siam.org/doi/10.1137/1.9781611972818.60", "verified_source_urls": [ "https://epubs.siam.org/doi/10.1137/1.9781611972818.60", "https://search.r-project.org/CRAN/refmans/TSclust/html/diss.CID.html" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "pages": "699--710" }, "doi": "10.1137/1.9781611972818.60" }, { "id": "shokoohi2017multidtw", "title": "Generalizing DTW to the multi-dimensional case requires an adaptive approach", "authors": [ "Mohammad Shokoohi-Yekta", "Bing Hu", "Hongxia Jin", "Jun Wang", "Eamonn Keogh" ], "year": 2017, "venue": "Data Mining and Knowledge Discovery", "publication_status": "published", "source_type": "original_research", "family": "multivariate channel coupling", "citation_role": "Boundary", "supported_claim": "Dependent multivariate DTW shares an alignment across channels, whereas independent DTW allows different channel alignments; the paper demonstrates that the choices can yield different classifications.", "evidence_boundary": "The two constructions express different synchronization assumptions. Neither should be called a universally correct multivariate generalization.", "verification_level": "Author manuscript text and journal metadata.", "primary_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5668684/", "verified_source_urls": [ "https://pmc.ncbi.nlm.nih.gov/articles/PMC5668684/", "https://pubmed.ncbi.nlm.nih.gov/29104448/" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "31", "number": "1", "pages": "1--31" }, "doi": "10.1007/s10618-016-0455-0", "version_or_access_note": "Online publication was in 2016; journal volume year is 2017. PMC text was searchable but a direct open later returned an access check." }, { "id": "ding2008comparison", "title": "Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures", "authors": [ "Hui Ding", "Goce Trajcevski", "Peter Scheuermann", "Xiaoyue Wang", "Eamonn Keogh" ], "year": 2008, "venue": "Proceedings of the VLDB Endowment", "publication_status": "published", "source_type": "original_research", "family": "comparative evaluation", "citation_role": "Contrast", "supported_claim": "This study compares representation and distance choices across multiple datasets rather than relying solely on the evaluations accompanying individual methods.", "evidence_boundary": "It is historical empirical evidence under the study's datasets and protocols. Its rankings cannot establish the best method for a current task or new data distribution.", "verification_level": "Author-hosted original paper and publication metadata.", "primary_url": "https://users.eecs.northwestern.edu/~peters/references/vldb08.pdf", "verified_source_urls": [ "https://users.eecs.northwestern.edu/~peters/references/vldb08.pdf", "https://cir.nii.ac.jp/crid/1360292621315687168" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "1", "number": "2", "pages": "1542--1552" }, "doi": "10.14778/1454159.1454226" }, { "id": "wu2022fastdtw", "title": "FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates", "authors": [ "Renjie Wu", "Eamonn J. Keogh" ], "year": 2022, "venue": "IEEE Transactions on Knowledge and Data Engineering", "publication_status": "published", "source_type": "original_research", "family": "implementation and runtime comparison", "citation_role": "Boundary", "supported_claim": "The study shows that an approximate algorithm's name or asymptotic description is insufficient evidence of a runtime advantage over carefully implemented exact constrained DTW.", "evidence_boundary": "Its conclusions depend on implementations, lengths, windows and approximation radii. It does not justify claiming that every exact DTW implementation always beats every approximate implementation.", "verification_level": "Author project page with final journal citation and experiment descriptions.", "primary_url": "https://wu.renjie.im/research/fastdtw-is-slow/", "verified_source_urls": [ "https://wu.renjie.im/research/fastdtw-is-slow/" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "34", "number": "8", "pages": "3779--3785" }, "doi": "10.1109/TKDE.2020.3033752", "version_or_access_note": "Accepted/online in 2020; final journal citation is 2022. The ICDE 2021 extended abstract is not counted as a second study." }, { "id": "yue2022ts2vec", "title": "TS2Vec: Towards Universal Representation of Time Series", "authors": [ "Zhihan Yue", "Yujing Wang", "Juanyong Duan", "Tianmeng Yang", "Congrui Huang", "Yunhai Tong", "Bixiong Xu" ], "year": 2022, "venue": "Proceedings of the AAAI Conference on Artificial Intelligence", "publication_status": "published", "source_type": "original_research", "family": "learned representation", "citation_role": "Tool", "supported_claim": "TS2Vec learns timestamp-level representations through hierarchical contrastive training and aggregates them to represent subsequences.", "evidence_boundary": "Classification, forecasting and anomaly-detection evidence supports representation utility. An embedding distance still requires a target-specific retrieval or clustering evaluation, including pooling and normalization choices.", "verification_level": "Official AAAI publication and abstract.", "primary_url": "https://ojs.aaai.org/index.php/AAAI/article/view/20881", "verified_source_urls": [ "https://ojs.aaai.org/index.php/AAAI/article/view/20881" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "36", "number": "8", "pages": "8980--8987" }, "doi": "10.1609/aaai.v36i8.20881" }, { "id": "herrmann2023adtw", "title": "Amercing: An intuitive and effective constraint for dynamic time warping", "authors": [ "Matthieu Herrmann", "Geoffrey I. Webb" ], "year": 2023, "venue": "Pattern Recognition", "publication_status": "published", "source_type": "original_research", "family": "regularized elastic alignment", "citation_role": "Definition", "supported_claim": "Amerced DTW adds a fixed cost to non-diagonal warping steps, providing a tunable alternative to a hard warping window or multiplicative weighting.", "evidence_boundary": "The additive penalty changes which alignments are preferred and needs an explicit tuning protocol. The paper's classification evidence does not prove universal retrieval superiority.", "verification_level": "Publisher metadata and abstract; author preprint cross-check.", "primary_url": "https://www.sciencedirect.com/science/article/pii/S0031320323000341", "verified_source_urls": [ "https://www.sciencedirect.com/science/article/pii/S0031320323000341", "https://arxiv.org/abs/2111.13314" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "137", "pages": "109333" }, "doi": "10.1016/j.patcog.2023.109333", "version_or_access_note": "Cite the 2023 formal article rather than counting the 2021 preprint as another method." }, { "id": "dong2023simmtm", "title": "SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling", "authors": [ "Jiaxiang Dong", "Haixu Wu", "Haoran Zhang", "Li Zhang", "Jianmin Wang", "Mingsheng Long" ], "year": 2023, "venue": "Advances in Neural Information Processing Systems", "publication_status": "published", "source_type": "original_research", "family": "similarity-assisted representation learning", "citation_role": "Tool", "supported_claim": "SimMTM uses learned series-level similarities to aggregate representations from masked examples for reconstruction during pretraining.", "evidence_boundary": "Similarity is part of the learning mechanism. Forecasting and classification evaluations do not by themselves validate a standalone semantic retrieval metric.", "verification_level": "Official NeurIPS publication; official code description.", "primary_url": "https://papers.neurips.cc/paper_files/paper/2023/hash/5f9bfdfe3685e4ccdbc0e7fb29cccf2a-Abstract-Conference.html", "verified_source_urls": [ "https://papers.neurips.cc/paper_files/paper/2023/hash/5f9bfdfe3685e4ccdbc0e7fb29cccf2a-Abstract-Conference.html", "https://github.com/thuml/SimMTM" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "36" } }, { "id": "goswami2024moment", "title": "MOMENT: A Family of Open Time-series Foundation Models", "authors": [ "Mononito Goswami", "Konrad Szafer", "Arjun Choudhry", "Yifu Cai", "Shuo Li", "Artur Dubrawski" ], "year": 2024, "venue": "Proceedings of the 41st International Conference on Machine Learning", "publication_status": "published", "source_type": "original_research", "family": "pretrained representation", "citation_role": "Tool", "supported_claim": "MOMENT provides pretrained time-series models and evaluates them across several tasks and limited-supervision settings.", "evidence_boundary": "The existence of pretrained embeddings does not establish a universal notion of similarity. Retrieval use needs relevant-query labels or another explicit target, training-data overlap checks, pooling, scaling and checkpoint specification.", "verification_level": "Official PMLR publication; official project and implementation documentation.", "primary_url": "https://proceedings.mlr.press/v235/goswami24a.html", "verified_source_urls": [ "https://proceedings.mlr.press/v235/goswami24a.html", "https://github.com/moment-timeseries-foundation-model/moment" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "235", "pages": "16115--16152" } }, { "id": "paparrizos2024survey", "title": "A Survey on Time-Series Distance Measures", "authors": [ "John Paparrizos", "Haojun Li", "Fan Yang", "Kaize Wu", "Jens E. d'Hondt", "Odysseas Papapetrou" ], "year": 2024, "venue": "arXiv", "publication_status": "preprint_survey", "source_type": "survey", "family": "taxonomy and literature discovery", "citation_role": "Definition", "supported_claim": "The survey organizes distance measures into lock-step, sliding, elastic, kernel, feature, model and embedding families, covering both univariate and multivariate formulations.", "evidence_boundary": "This is a survey, not a new head-to-head experiment. Original method papers remain the sources for individual definitions and guarantees; the family taxonomy is not a claim that all methods are implemented in EchoTime.", "verification_level": "Author-uploaded arXiv record; author publication page.", "primary_url": "https://arxiv.org/abs/2412.20574", "verified_source_urls": [ "https://arxiv.org/abs/2412.20574", "https://jensdhondt.com/publication/book_chapter_2024" ], "checked_on": "2026-10-29", "bibtex_type": "misc", "bibtex_fields": { "eprint": "2412.20574", "archivePrefix": "arXiv", "primaryClass": "cs.DB" }, "version_or_access_note": "The verified record is arXiv:2412.20574v1 (2024). A formally published successor was not established in this check." }, { "id": "dhondt2025multivariate", "title": "A Structured Study of Multivariate Time-Series Distance Measures", "authors": [ "Jens E. d'Hondt", "Haojun Li", "Fan Yang", "Odysseas Papapetrou", "John Paparrizos" ], "year": 2025, "venue": "Proceedings of the ACM on Management of Data", "publication_status": "published", "source_type": "original_research", "family": "multivariate comparative evaluation", "citation_role": "Contrast", "supported_claim": "The study jointly examines 30 distance measures, 13 normalizations, two channel-dependency models, 30 datasets and three downstream tasks, including supervised and unsupervised parameter selection.", "evidence_boundary": "Reported rankings and normalization effects are conditional on the study design. This supports testing preprocessing and channel assumptions, not claiming one distance or normalization is universally optimal.", "verification_level": "Publisher metadata and abstract; author institution record.", "primary_url": "https://doi.org/10.1145/3725258", "verified_source_urls": [ "https://doi.org/10.1145/3725258", "https://research.tue.nl/en/publications/a-structured-study-of-multivariate-time-series-distance-measures/", "https://jensdhondt.com/publication/sigmod_2025" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "3", "number": "3", "pages": "121:1--121:29" }, "doi": "10.1145/3725258" }, { "id": "schaefer2025sofa", "title": "Fast and Exact Similarity Search in Less than a Blink of an Eye", "authors": [ "Patrick Schäfer", "Jakob Brand", "Ulf Leser", "Botao Peng", "Themis Palpanas" ], "year": 2025, "venue": "Proceedings of the 41st IEEE International Conference on Data Engineering", "publication_status": "published", "source_type": "original_research", "family": "exact Euclidean retrieval indexing", "citation_role": "Tool", "supported_claim": "SOFA combines SFA-based summaries with a tree index and lower-bound filtering for exact Euclidean nearest-neighbor search.", "evidence_boundary": "The target is exact Euclidean retrieval; speed comparisons do not establish superior semantic similarity or transfer automatically to DTW. Index construction, memory, data and implementation must be included in a local comparison.", "verification_level": "Author-hosted original paper and official ICDE 2025 accepted-paper list; preprint cross-check.", "primary_url": "https://helios2.mi.parisdescartes.fr/~themisp/publications/icde25-sofa.pdf", "verified_source_urls": [ "https://helios2.mi.parisdescartes.fr/~themisp/publications/icde25-sofa.pdf", "https://ieee-icde.org/2025/research-papers/", "https://arxiv.org/abs/2411.17483" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": {}, "version_or_access_note": "The 2024 preprint and 2025 conference article are one work. DOI omitted because the publisher endpoint did not resolve in this check." }, { "id": "ning2025tsrag", "title": "TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster", "authors": [ "Kanghui Ning", "Zijie Pan", "Yu Liu", "Yushan Jiang", "James Zhang", "Kashif Rasul", "Anderson Schneider", "Lintao Ma", "Yuriy Nevmyvaka", "Dongjin Song" ], "year": 2025, "venue": "Advances in Neural Information Processing Systems", "publication_status": "published", "source_type": "original_research", "family": "retrieval-augmented forecasting", "citation_role": "Boundary", "supported_claim": "TS-RAG retrieves segments through pretrained encoders and integrates retrieved patterns with a forecasting model using an adaptive mixer.", "evidence_boundary": "The reported downstream objective is forecasting. Forecast improvement alone does not establish general semantic nearest-neighbor accuracy, reliable causal analogues or superiority of the retrieval distance itself.", "verification_level": "Official NeurIPS publication and abstract.", "primary_url": "https://proceedings.nips.cc/paper_files/paper/2025/hash/eed25c037bc08afcbefab6f7a6b700e0-Abstract-Conference.html", "verified_source_urls": [ "https://proceedings.nips.cc/paper_files/paper/2025/hash/eed25c037bc08afcbefab6f7a6b700e0-Abstract-Conference.html" ], "checked_on": "2026-10-29", "bibtex_type": "inproceedings", "bibtex_fields": { "volume": "38" } }, { "id": "azzari2026tsdistances", "title": "Algorithm 1061: tsdistances: A High-Performance Python Library for Time Series Distances with GPU Support", "authors": [ "Alberto Azzari", "Andrea Cracco", "Francesco Masillo", "Pietro Sala" ], "year": 2026, "venue": "ACM Transactions on Mathematical Software", "publication_status": "published", "source_type": "original_research", "family": "CPU and GPU distance software", "citation_role": "Tool", "supported_claim": "The software paper describes CPU and GPU implementations of time-series distances, with particular attention to elastic algorithms and parallel computation.", "evidence_boundary": "The publication establishes a relevant implementation source. This literature check did not run its benchmarks or verify interoperability; reported speedups must not be presented as EchoTime measurements.", "verification_level": "Author university publication record and abstract; independent bibliographic corroboration. Publisher full text unavailable.", "primary_url": "https://www.di.univr.it/?ent=pubbdip&id=1192651&lang=en", "verified_source_urls": [ "https://www.di.univr.it/?ent=pubbdip&id=1192651&lang=en", "https://dblp.org/pid/351/1189.html" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "52", "number": "1" }, "doi": "10.1145/3802579", "version_or_access_note": "ACM returned HTTP 403 during the check. The university record verifies year, authors, journal, volume, issue and DOI; this is not a full-text implementation audit." }, { "id": "bandt2002permutation", "title": "Permutation Entropy: A Natural Complexity Measure for Time Series", "authors": [ "Christoph Bandt", "Bernd Pompe" ], "year": 2002, "venue": "Physical Review Letters", "publication_status": "published", "source_type": "original_research", "family": "ordinal-pattern representation", "citation_role": "Definition", "supported_claim": "Permutation entropy uses the order patterns of neighboring observations to characterize temporal complexity.", "evidence_boundary": "An ordinal-pattern histogram is a representation, and a distance between such histograms is an additional design choice. Embedding dimension, delay and tie handling affect the representation; the original paper does not validate every histogram distance.", "verification_level": "Official publisher metadata and abstract.", "primary_url": "https://doi.org/10.1103/PhysRevLett.88.174102", "verified_source_urls": [ "https://doi.org/10.1103/PhysRevLett.88.174102" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "88", "pages": "174102" }, "doi": "10.1103/PhysRevLett.88.174102" }, { "id": "lin1991js", "title": "Divergence Measures Based on the Shannon Entropy", "authors": [ "Jianhua Lin" ], "year": 1991, "venue": "IEEE Transactions on Information Theory", "publication_status": "published", "source_type": "original_research", "family": "probability-distribution divergence", "citation_role": "Definition", "supported_claim": "Jensen-Shannon divergence compares probability distributions through entropy-based mixture differences and remains finite without the support restrictions of directed KL divergence.", "evidence_boundary": "The logarithm base sets the scale. Probability normalization is required; applying the divergence to a spectrum or ordinal histogram compares those summaries, which can discard time-domain information.", "verification_level": "Publisher metadata and abstract; original paper text.", "primary_url": "https://ieeexplore.ieee.org/abstract/document/61115/authors", "verified_source_urls": [ "https://ieeexplore.ieee.org/abstract/document/61115/authors", "https://www.cise.ufl.edu/~anand/sp06/jensen-shannon.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "37", "number": "1", "pages": "145--151" }, "doi": "10.1109/18.61115" }, { "id": "endres2003jsmetric", "title": "A New Metric for Probability Distributions", "authors": [ "Dominik M. Endres", "Johannes E. Schindelin" ], "year": 2003, "venue": "IEEE Transactions on Information Theory", "publication_status": "published", "source_type": "original_research", "family": "probability-distribution metric", "citation_role": "Boundary", "supported_claim": "The paper establishes a metric proportional to the square root of equal-weight Jensen-Shannon divergence.", "evidence_boundary": "The square-root transformation is essential to this metric statement. A metric on probability summaries need not distinguish the underlying raw series when different series produce the same summary.", "verification_level": "Author institution repository metadata and original paper.", "primary_url": "https://research-repository.st-andrews.ac.uk/handle/10023/1591", "verified_source_urls": [ "https://research-repository.st-andrews.ac.uk/handle/10023/1591", "https://research-repository.st-andrews.ac.uk/bitstream/10023/1591/1/Endres2003-IEEETransInfTheory49-NewMetric.pdf" ], "checked_on": "2026-10-29", "bibtex_type": "article", "bibtex_fields": { "volume": "49", "number": "7", "pages": "1858--1860" }, "doi": "10.1109/TIT.2003.813506" } ] }