Pith. sign in

REVIEW 5 major objections 5 minor 30 references

MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The MINT pipeline treats the tensor of stacked Mplots as a mineable object whose CPD components recover shared cross-sensor temporal patterns.

desk verdict The stacked-Mplot tensor is a genuinely new primitive, but the load-bearing Robust PCA cleaning step is never ablated, so the co-clustering claim is not yet fully supported. read the letter →

arxiv 2608.04157 v1 pith:7C4Z33WE submitted 2026-08-04 cs.LG

Kaamil Kaka , Audrey Der , Evangelos E. Papalexakis , Zachary Zimmerman , Vikram Jayaram This is my paper · ORCID
classification cs.LG
keywords timeseriesdataminingrecurrenceplotsMplotsself-similaritymatricestensordecompositioncanonicalpolyadicco-clusteringrobustPCA
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces a new time series mining primitive: instead of compressing each series to a matrix profile vector, it keeps each series' full pairwise self-similarity matrix (its Mplot) and stacks all of them into a third-order tensor. The MINT pipeline cleans each Mplot with nonnegative robust PCA, mean-centers the low-rank part, and runs canonical polyadic decomposition on the stacked tensor. The claim is that the resulting factor matrices co-cluster cross-sensor patterns: the same components index which sensors, which subsequences, and which time intervals participate in a shared phenomenon. The authors show the pipeline separates Christmas events from seasonal electricity demand across 18 countries, and back it with a 50-trial noise-injection experiment where four co-clustering properties pass in 92-100 percent of trials, far above the chance bounds they derive.

What carries the argument

The load-bearing object is the stacked, cleaned Mplot tensor. An Mplot is the matrix of Euclidean distances between every pair of sliding-window subsequences of one time series, so it records when a series is self-similar to itself at different lags. Each Mplot is pooled, then decomposed by nonnegative robust PCA into a low-rank matrix (periodic self-similarity) and a sparse residual; the low-rank matrices are mean-centered to remove each series' baseline self-similarity level, and stacked into a third-order tensor. CPD then decomposes this tensor into three factor matrices, giving per-component sensor, subsequence, and time loadings. The mean-centering is what keeps CPD from spending a component ranking sensors by overall regularity; the nonnegative low-rank cleaning is what concentrates the time-aligned structure.

What would settle it

Run the full 50-trial MINT protocol on a dataset of independent random-walk series with no shared motifs: if the top-s sensors and top-s intervals still align in a majority of trials, the co-clustering is a representation artifact. Alternatively, on synthetic data with planted aligned windows, inspect the cleaning output directly: if the planted windows appear in the sparse residual S instead of the low-rank matrix L, the pipeline's central assumption fails.

Watch

Extended reading notes

Core claim

The central discovery is that full self-similarity information, pooled, cleaned, and stacked, concentrates cross-series temporal structure well enough for standard tensor decomposition to extract it. For a dataset of N univariate series of length n with subsequence window m, MINT computes N Mplots, pools them to k×k, splits each into low-rank plus sparse via nonnegative robust PCA, mean-centers the low-rank slices, and stacks them into an N×k×k tensor. CPD factorizes this tensor into a sensor-mode matrix A and two subsequence/time-mode matrices B and C; the authors read each rank-1 component as a shared pattern. In the OPSD case study, two components correspond to Christmas spikes and long-scale seasonal demand, and in the synthetic and injected-noise experiments the components align with planted windows across sensors.

Load-bearing premise

The central premise is that the nonnegative robust PCA cleaning, which the paper notes lacks recovery guarantees, keeps time-aligned periodic structure in the low-rank component of each Mplot while discarding per-sensor baseline self-similarity.

Editorial extensions

If this is right

  • Every decomposition component directly indexes the sensors, subsequences, and time intervals participating in a shared pattern, so cross-series structure is recoverable without labels or learned embeddings.
  • In the four case studies, planted or reference-aligned windows are found in the top loadings in 92-100 percent of 50 random trials, with null hypotheses rejected at Bonferroni-corrected significance.
  • The OPSD case study shows that short-scale holiday spikes and long-scale seasonal demand separate into distinct components within the same decomposition.
  • The pipeline's O(N n^2 + N k^3) complexity makes large collections of series analyzable without per-series visual inspection, with robust PCA as the practical bottleneck.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The authors leave implicit a boundary condition: the method targets highly regular datasets whose motifs recur at regular intervals; a natural extension would test how component stability degrades as motif periodicity is corrupted.
  • A testable extension would verify the cleaning step directly: on synthetic data with planted cross-series windows, check that the windows live in the low-rank component rather than the sparse residual or the mean-centered offset.
  • Because the tensor construction is agnostic to the similarity measure, the same stacking-and-decomposition scheme could be tried with warping-invariant or phase-space self-similarity matrices, which the authors list as future work.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes tensorized self-similarity matrices as a new time series data mining primitive: for a collection of N univariate series, it computes an Mplot (pairwise subsequence distance matrix) for each series, applies per-Mplot Nonnegative Robust PCA followed by mean-centering of the low-rank components, stacks these into a 3-tensor, and extracts CPD factors. The authors claim that the resulting factor matrices co-cluster cross-sensor temporal patterns, as demonstrated by an OPSD electricity demand case study and by a synthetic noise-injection robustness study on four real datasets (Taipei MRT, LargeST traffic, Electricity Load Diagrams, CARE wind turbine). In the synthetic study, they plant transformed traces and report that four hand-defined properties P1-P4 are satisfied in 92-100% of 50 trials, rejecting null hypotheses at Bonferroni-corrected significance. The paper includes code and detailed chance-rate bounds in Appendix B.

Significance. If the central claim were established, the paper would introduce a reusable primitive with interpretable decompositions and a clearly specified pipeline, with machine-checkable code and a transparent null model in Appendix B. The idea of treating pooled self-similarity matrices as a tensor to be decomposed is potentially valuable for exploratory analysis of multi-sensor time series, and the authors are appropriately cautious in limiting the claim to 'highly regular datasets containing motifs at regular intervals.' However, the current evidence is not yet sufficient: the pipeline is not compared with any alternative representation or co-clustering method, the role of the RPCA cleaning step is untested by ablation, and the hypothesis tests are framed against an arbitrary 0.7 threshold rather than the analytically derived chance rates. These issues are fixable within the scope of a revision.

major comments (5)
  1. [Section 5.3; Appendix B] The hypothesis tests are specified as H0,i: p_i <= 0.7, but the random-selection model in Appendix B bounds the chance pass rates at at most 0.00584, 0.176, 0.325, and 0.570 for P1-P4, respectively. Rejecting p_i > 0.7 only establishes that the pass rate exceeds an arbitrary threshold, not that MINT outperforms random selection; for P4 a procedure with pass rate 0.71 would reject H0,4 while remaining only marginally above the chance bound of 0.570. The p-values in Table 6.1 should therefore be computed against the chance model (or an exact binomial test on the chance rates), and the selected ranks R for each trial should be reported, since the P4 threshold floor(0.7R) depends on R.
  2. [Section 3, Step 3; Section 5.1] The cleaning step, per-Mplot Nonnegative Robust PCA into low-rank plus sparse followed by mean-centering of the low-rank matrices, is the load-bearing mechanism that is supposed to preserve cross-series co-occurrence while discarding per-sensor baseline self-similarity, but it is never isolated. No ablation compares the full pipeline to stacking raw Mplots, stacking only the sparse components, or skipping mean-centering; footnote 1 explicitly defers recovery guarantees for the nonnegative-constrained variant. Because the synthetic windows in Section 5.1 are constructed to produce block-like low-rank Mplot structure, those experiments do not test the preservation of aligned periodic structure in real data. The authors should add ablations (e.g., raw Mplots, sparse-only, no mean-centering, and no RPCA) under the same noise-injection protocol.
  3. [Section 5.1; Section 6] The experimental protocol evaluates MINT in isolation and provides no comparison with any baseline method on the same tasks. The paper claims that the tensorized self-similarity matrix 'concentrates cross-series temporal structure' and that this enables analysis that per-series methods cannot, but the only quantitative evidence is a robustness test of MINT against MINT's own planted windows. Baselines such as CPD applied directly to the raw data tensor, CPD on stacked Mplots without cleaning, NMF or PCA on the raw data, RP-GCN, or a Matrix-Profile-based co-clustering method should be run under the same protocol to substantiate the representational claim.
  4. [Section 4.1] The OPSD case study is anecdotal: Component 2 is labeled 'Christmas' and Component 3 'seasonal' by visual inspection of the highest |C| loadings, with no quantitative match to holiday dates or to a seasonal decomposition, and several series are imputed with random noise before analysis. The statement that no single-series method finds this separation is not supported by any experiment with a single-series method, and Figure 4.1 alone does not establish that the decomposition separates the two kinds of demand signature. Either add a quantitative evaluation (e.g., correlation of component loadings with known holiday indicators, comparison with per-series Matrix Profile or spectral methods) or recast the section as an illustrative example.
  5. [Section 5.4; Table 6.1] The free parameters m, k, s, b, f, and R are fixed per dataset without sensitivity analysis, and the ranks selected by the Kneedle rule are not reported. The threshold in P4 is floor(0.7R), and the random-pass bound in Table B.1 is 0.570 for R=2, so the evidential strength of P4 depends heavily on the rank distribution actually encountered. The authors should report the distribution of R over the 50 trials and provide sensitivity analyses over at least m, s, f, and b to show that the 92-100% pass rates are not an artifact of a single parameter configuration.
minor comments (5)
  1. [Definition 3.1] The definition of the Mplot refers to AB-Join distance profiles, but the pipeline uses Self-Join Mplots; please state explicitly that the Mplots are computed on z-normalized subsequences and whether the self-join excludes trivial matches.
  2. [Section 5.4] Trials that yielded NaNs in Mplots were discarded and re-tried on a new seed; please report how many trials were discarded, since systematic discarding could bias the estimated pass rates.
  3. [Section 4.1] Please report the number of NaN-imputed series in the OPSD dataset and the parameters of the random-noise imputation, because random imputation can create spurious structure in the tensor.
  4. [Table 6.1] The table reports p-values only; exact binomial 95% confidence intervals for the proportions would be more informative, especially for the 0.92 estimate on CARE to Compare.
  5. [Section 2] The sentence 'We leave formal verification for future work, but we propose that ... constitutes an existence proof' is internally inconsistent; an existence proof cannot be deferred to future work, so please rephrase.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MINT's core stacking-and-decomposition claim is validated against planted structure with no fitted parameter renamed as prediction.

full rationale

The derivation chain is self-contained. MINT's central claim is that stacking cleaned per-series Mplots into a 3-tensor and applying CPD yields factor matrices whose components correspond to time-aligned cross-series patterns. The Mplot is defined in Definition 3.1; the tensor construction, mean-centering, and CPD are specified in Section 3, and the noise-injection protocol in Section 5 uses planted windows W and transformation labels θ1-θ3 as external ground truth. No parameter is fitted to these labels or windows: the rank R is chosen by an elbow heuristic on reconstruction error, and the Robust PCA parameters are inherited from Stable PCP or estimated from each Mplot. The recovery criteria P1-P4 are evaluated against the planted structure but are not inputs to the algorithm, so the validation is not tautological. The paper candidly notes in footnote 1 that recovery guarantees for the nonnegative-constrained Robust PCA variant are deferred to future work; this is an acknowledged limitation, not a circular step. Similarly, Section 2 says formal verification of the co-clustering setting is left for future work, which is an honest admission rather than a disguised reuse of inputs. Self-citations to Mplots [22] and SCAMP [30] support the choice of representation and implementation, but the stacking-and-decomposition contribution is new and is tested on external datasets (OPSD, MRT, LargeST, CARE), so the citations are not load-bearing in the sense of forcing the conclusion. No equation in the paper reduces to its own input by construction, and no fitted parameter is later relabeled as a prediction.

Assumptions & free parameters 7 free parameters · 7 assumptions · 1 invented entities

The central claim depends on several hand-set hyperparameters (m, k, s, b, f, R, and the P4 threshold) and on domain assumptions about the faithfulness of Mplots, the decomposition of the Mplot into low-rank and sparse parts, and the validity of the synthetic noise-injection null. No independent theoretical guarantees are provided for the core NR PCA step.

free parameters (7)
  • subsequence length m = 146 (MRT), 95 (LargeST, Electricity), 140 (CARE)
    Chosen per dataset to match motif lengths (week or day). Affects which structures appear in the Mplot and the tensor.
  • pooling parameter k = ceil(3n/m)
    Controls Mplot resolution; pixel width c_k approximately m/3. Hand-set relation between n and m.
  • top-s parameter s = 6
    Number of top sensors and intervals per component; chosen so the validation properties remain nontrivial.
  • transformed fraction b = ceil(0.25N)
    Number of sensors made completely random or mostly random in the noise injection; hand-set.
  • window coverage f = 0.10
    Planted windows cover 10% of each time series; part of the synthetic validation design.
  • rank R = estimated by Kneedle elbow in [2..8], default 2
    CPD rank is fit to the reconstruction error curve for each tensor.
  • P4 threshold floor(0.7R) = floor(0.7R)
    Threshold for a significant majority of Mostly Normal components; chosen to keep small ranks (R = 2) valid.
assumptions (7)
  • domain assumption Mplots computed with Euclidean distance faithfully represent subsequence self-similarity.
    Inherited from the authors' Mplot paper [22]; the primitive is built on it.
  • domain assumption The low-rank part of an Mplot after NR PCA contains recurring or periodic structure; the sparse part contains anomalies.
    Section 3, step 3; no proof is given for the nonnegative-constrained variant.
  • domain assumption Mean-centering low-rank matrices removes per-sensor self-similarity baselines so CPD ranks co-occurrence rather than energy.
    Section 3, justification paragraph.
  • standard math CPD of the stacked tensor yields interpretable, unique-enough components.
    Standard tensor decomposition assumption; no identifiability check is reported.
  • domain assumption The random-selection model in Appendix B validly upper-bounds chance pass rates for the synthetic test.
    Assumes top-s sensors and intervals are selected uniformly at random; actual structuredness may alter the null.
  • domain assumption Mplots are forgiving with respect to the exact value of m.
    Section 5.4; used to justify hand-set subsequence lengths.
  • domain assumption SCAMP computes Mplots correctly.
    Tooling assumption from the SCAMP library [30].
invented entities (1)
  • Tensorized self-similarity matrix (MINT primitive)
    purpose: A data object formed by stacking per-series Mplots into a 3-tensor for CPD mining; the paper's central novelty.
    No external falsifiable prediction beyond the paper's own experiments; the case studies (e.g., Christmas vs. seasonal patterns) are post-hoc visual interpretations, not pre-registered predictions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining." pith.science (2026). https://pith.science/paper/7C4Z33WE

@misc{pith2026260804157,
  author       = {Pith},
  title        = {Pith review of: MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7C4Z33WE}},
  note         = {Machine review of arXiv:2608.04157}
}
abstract

Recurrence plots are a time series data mining primitive applied to a variety of domains (e.g. star light curves, sound waveforms, CCT telemetry). This work proposes tensorized self-similarity matrices as a primitive for univariate time series datasets ($N\times n$) of $N$ time series of length $n$ with a subsequence window of length $m$, and whose tensor-based nature is naturally extensible to multivariate datasets. The proposed method to compute this primitive computes dot plots of size $N \times (n-m+1) \times (n-m+ 1)$ from these datasets, where the subsequent tensor is mined using tensor decomposition methods to mine for co-clustered patterns. We demonstrate our results in mass rapid transit, electricity demand, wind turbine, and car traffic data, finding the MINT pipeline effectively co-clusters cross-sensor patterns in highly regular datasets containing motifs at regular intervals.

Figures

Figures reproduced from arXiv: 2608.04157 by the authors.

Figure 1.1
Figure 1.1. NMF applied to the synthetic dataset. Top) Representative series from each phase group over the first 30 days, gray shading marks the first sawtooth. Bottom Left) The temporal factor WT (time × com￾ponents). Bottom Right) The series-mode factor HT , columns sorted by ground-truth phase group, revealing a block structure consistent with T1, T2, T3. To illustrate what is lost when the self-similarity representation is… view at source ↗
Figure 1.2
Figure 1.2. MINT applied to the same dataset [PITH_FULL_IMAGE:figures/full_fig_p002_1_2.png] view at source ↗
Figure 3.1
Figure 3.1. Top) A time series T of Mass Rapid Transit usage. Middle) A subsequence Q from T Bottom) A distance profile computed using Mueen’s Algorithm for Similarity Search [17] of Q against T. quence level—for example, the Matrix Profile in MP￾NCMI clustering [12]—they ultimately associate only a single label to each time series. Moreover, they do not natively detect time-aligned correlations between sub￾sequences in differe… view at source ↗
Figures from the paper (5 more)
Figure 4.1
Figure 4.1. Figure 4.1: (1) Transformed factor matrix C for the OPSD dataset. (2) Analysis of Components 2 and 3 indicates that they correspond to distinctive Christmas and seasonal electricity demand patterns, respectively. Portugal. The wind farm data contains 22 datasets with a total of …
Figure 3
Figure 3. Figure 3: and Figure 3.1. Sample time series from [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 4
Figure 4. Figure 4: depicts this analysis applied on factor [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5.1
Figure 5.1. Figure 5.1: Four subsequences from four time series from the LargeST dataset, surrounding one disjoint window t. The Completely Random θ1(Ti), Mostly Random θ2(Ti), and Mostly Normal θ3(Ti) transforms on the original Ti are shown. In Mostly Random subsequences, we see t contains…
Figure 6.1
Figure 6.1. Figure 6.1: Last week of data for four time series from the CARE to Compare dataset. Observe the time￾aligned degenerate flatlines. Observe that properties P2, P3, and P4 were satis￾fied on all 50 trials for the four datasets. However, only 92% of trials satisfied P1 in the CARE…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 21 canonical work pages

  1. [1]

    Bolivar, S.-C

    S. Bolivar, S.-C. Huang, and R. Chen , Analysis of tensor time series , Annual Review of Statistics and Its Application, 13 (2026), pp. 369--398, http://dx.doi.org/10.1146/annurev-statistics-042424-063308 doi: 10.1146/annurev-statistics-042424-063308

    work page doi:10.1146/annurev-statistics-042424-063308 2026

  2. [2]

    Chang, J

    J. Chang, J. He, L. Yang, and Q. Yao , Modelling matrix time series via a tensor CP -decomposition , Journal of the Royal Statistical Society Series B: Statistical Methodology, 85 (2023), pp. 127--148, http://dx.doi.org/10.1093/jrsssb/qkac011 doi: 10.1093/jrsssb/qkac011 , https://doi.org/10.1093/jrsssb/qkac011

    work page doi:10.1093/jrsssb/qkac011 2023

  3. [3]

    Y. Chen, S. Su, and H. Yang , Convolutional neural network analysis of recurrence plots for anomaly detection , International Journal of Bifurcation and Chaos, 30 (2020), p. 2050002, http://dx.doi.org/10.1142/S0218127420500029 doi: 10.1142/S0218127420500029 , https://doi.org/10.1142/S0218127420500029

    work page doi:10.1142/s0218127420500029 2020

  4. [4]

    Eckmann, S

    J.-P. Eckmann, S. O. Kamphorst, and D. Ruelle , Recurrence plots of dynamical systems , Europhysics Letters, 4 (1987), p. 973, http://dx.doi.org/10.1209/0295-5075/4/9/004 doi: 10.1209/0295-5075/4/9/004 , https://doi.org/10.1209/0295-5075/4/9/004

    doi:10.1209/0295-5075/4/9/004 1987

  5. [5]

    Gavish and D

    M. Gavish and D. L. Donoho , The optimal hard threshold for singular values is 4/ 3 , IEEE Transactions on Information Theory, 60 (2014), pp. 5040--5053, http://dx.doi.org/10.1109/TIT.2014.2323359 doi: 10.1109/TIT.2014.2323359

    arXiv 2014

  6. [6]

    Goswami , A brief introduction to nonlinear time series analysis and recurrence plots , Vibration, 2 (2019), pp

    B. Goswami , A brief introduction to nonlinear time series analysis and recurrence plots , Vibration, 2 (2019), pp. 332--368

    work page 2019

  7. [7]

    C. Gück, C. M. A. Roelofs, and S. Faulstich , CARE to compare: A real-world benchmark dataset for early fault detection in wind turbine data , Data, 9 (2024), http://dx.doi.org/10.3390/data9120138 doi: 10.3390/data9120138 , https://www.mdpi.com/2306-5729/9/12/138

    doi:10.3390/data9120138 2024

  8. [8]

    Jayaram, B

    V. Jayaram, B. E. Usevitch, and O. M. Kosheleva , Detection from hyperspectral images compressed using rate distortion and optimization techniques under JPEG2000 part 2 , in Proceedings of the 11th IEEE Digital Signal Processing Workshop (DSP'04), Taos Ski Valley, New Mexico, USA, Aug. 2004, pp. 111--114

    work page 2004

Show all 30 references
  1. [9]

    Kang, T.-H

    H. Kang, T.-H. Lee, and J. Lee , A graph convolutional network for time series classification using recurrence plots , Applied Intelligence, 55 (2025), p. 972, http://dx.doi.org/10.1007/s10489-025-06841-3 doi: 10.1007/s10489-025-06841-3 , https://doi.org/10.1007/s10489-025-06841-3

    2025 doi

  2. [10]

    Keogh and J

    E. Keogh and J. Lin , Clustering of time-series subsequences is meaningless: Implications for previous and future research , Knowledge and Information Systems, 8 (2005), pp. 154--177, http://dx.doi.org/10.1007/s10115-004-0172-7 doi: 10.1007/s10115-004-0172-7

    2005 doi

  3. [11]

    Kossaifi, Y

    J. Kossaifi, Y. Panagakis, A. Anandkumar, and M. Pantic , TensorLy : Tensor learning in Python , Journal of Machine Learning Research (JMLR), 20 (2019)

    2019

  4. [12]

    Li and M

    H. Li and M. Chen , Time series clustering based on normal cloud model and complex network , Applied Soft Computing, 148 (2023), p. 110876, http://dx.doi.org/https://doi.org/10.1016/j.asoc.2023.110876 doi: https://doi.org/10.1016/j.asoc.2023.110876 , https://www.sciencedirect....

    2023

  5. [13]

    X. Liu, Y. Xia, Y. Liang, J. Hu, Y. Wang, L. Bai, C. Huang, Z. Liu, B. Hooi, and R. Zimmermann , LargeST : A benchmark dataset for large-scale traffic forecasting , in Advances in Neural Information Processing Systems (NeurIPS) 2023 — Datasets & Benchmarks Track, 2023

    2023

  6. [14]

    March, S

    T. March, S. Chapman, and R. Dendy , Recurrence plot statistics and the effect of embedding , Physica D: Nonlinear Phenomena, 200 (2005), pp. 171--184

    2005

  7. [15]

    Marwan, M

    N. Marwan, M. Carmen Romano , M. Thiel, and J. Kurths , Recurrence plots for the analysis of complex systems , Physics Reports, 438 (2007), pp. 237--329, http://dx.doi.org/https://doi.org/10.1016/j.physrep.2006.11.001 doi: https://doi.org/10.1016/j.physrep.2006.11.001 , https:...

    2007 doi

  8. [16]

    Marwan and J

    N. Marwan and J. Kurths , Nonlinear analysis of bivariate data with cross recurrence plots , Physics Letters A, 302 (2002), pp. 299--307, http://dx.doi.org/https://doi.org/10.1016/S0375-9601(02)01170-2 doi: https://doi.org/10.1016/S0375-9601(02)01170-2 , https://www.sciencedir...

    2002 doi

  9. [17]

    Mueen, S

    A. Mueen, S. Zhong, Y. Zhu, M. Yeh, K. Kamgar, K. Viswanathan, C. Gupta, and E. Keogh , The fastest similarity search algorithm for time series subsequences under Euclidean distance , August 2022. http://www.cs.unm.edu/ mueen/FastestSimilaritySearch.html

    2022

  10. [18]

    Paparrizos, F

    J. Paparrizos, F. Yang, and H. Li , Bridging the gap: A decade review of time-series clustering methods. , arXiv preprint arXiv:2412.20582, (2024)

    2024 arXiv

  11. [19]

    Rakthanmanon, B

    T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh , Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping , ACM Transactions on Knowledge Discovery from Data (TKDD), 7 (2013), pp. 1--31

    2013

  12. [20]

    M. C. Romano, M. Thiel, J. Kurths, and W. von Bloh , Multivariate recurrence plots , Physics Letters A, 330 (2004), pp. 214--223, https://api.semanticscholar.org/CorpusID:5746162

    2004

  13. [21]

    Sato and S

    K. Sato and S. Ono , Joint background-anomaly-noise decomposition for robust hyperspectral anomaly detection via constrained convex optimization , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19 (2026), pp. 20217--20235, http://dx.doi.org/1...

    2026

  14. [22]

    Shahcheraghi, R

    M. Shahcheraghi, R. Mercer, J. M. D. A. Rodrigues, A. Der, H. F. S. Gamboa, Z. Zimmerman, K. Mauck, and E. J. Keogh , Introducing Mplots : scaling time series recurrence plots to massive datasets , J. Big Data, 11 (2024), p. 96, https://doi.org/10.1186/s40537-024-00954-1

    2024 doi

  15. [23]

    N. D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, and C. Faloutsos , Tensor decomposition for signal processing and machine learning , IEEE Transactions on Signal Processing, 65 (2017), pp. 3551--3582, https://ieeexplore.ieee.org/abstract/document/7891546/

    2017

  16. [24]

    D. F. Silva, V. M. D. Souza, and G. E. Batista , Time series classification using compression distance of recurrence plots , in 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 687--696, http://dx.doi.org/10.1109/ICDM.2013.128 doi: 10.1109/ICDM.2013.128

    2013 doi

  17. [25]

    Trindade , ElectricityLoadDiagrams20112014

    A. Trindade , ElectricityLoadDiagrams20112014 . https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014. UCI Machine Learning Repository

  18. [26]

    Uhlmann , Vergleich der hypergeometrischen mit der Binomial-Verteilung , Metrika, 10 (1966), pp

    W. Uhlmann , Vergleich der hypergeometrischen mit der Binomial-Verteilung , Metrika, 10 (1966), pp. 145--158, http://dx.doi.org/10.1007/BF02613425 doi: 10.1007/BF02613425

    1966 doi

  19. [27]

    C.-C. M. Yeh, Y. Zhu, H. A. Dau, A. Darvishzadeh, M. Noskov, and E. Keogh , Online amnestic DTW to allow real-time golden batch monitoring , in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, New York, NY, USA, 2019, A...

    2019

  20. [28]

    C. M. Yeh, Y. Zhu, L. Ulanova, N. Begum, Y. Ding, H. A. Dau, D. F. Silva, A. Mueen, and E. J. Keogh , Matrix Profile I: all pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets , in IEEE 16th International Conference on Data Mini...

    2016

  21. [29]

    Z. Zhou, X. Li, J. Wright, E. Cand \`e s, and Y. Ma , Stable principal component pursuit , in 2010 IEEE International Symposium on Information Theory, ISIT 2010 - Proceedings, IEEE International Symposium on Information Theory - Proceedings, 2010, pp. 1518--1522, http://dx.doi...

    2010

  22. [30]

    Zimmerman, K

    Z. Zimmerman, K. Kamgar, N. S. Senobari, B. Crites, G. J. Funning, P. Brisk, and E. J. Keogh , Matrix Profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond , in Proceedings of the ACM Symposium on Cloud Computin...

    2019

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.