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REVIEW 4 major objections 5 minor 32 references

Towards Explainable Sequential Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read By discretizing multivariate time series into polyadic event logs and mining data-enriched Poly-DECLARE temporal clauses, this paper builds a classification pipeline whose decisions are both accurate and human-readable.

desk verdict Solid formal extension of DECLARE to polyadic dataful logs, but the abstract's 'outperform SOTA' claim is refuted by the paper's own tables on two of four datasets. read the letter →

arxiv 2505.23624 v1 pith:XPQIWES5 submitted 2025-05-29 cs.DB cs.AI

Giacomo Bergami , Emma Packer , Kirsty Scott , Silvia Del Din This is my paper
classification cs.DBcs.AI
keywords VerifiedAIeXplainable(XAI)polyadiclogsDataTrendsPoly-DECLAREMultivariateTimeSeriesClassificationspecificationminingCatch22
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 proposes EMeriTAte+DF, a pipeline that rewrites multivariate time series (MTS) into polyadic event logs, where each timestamp can hold several concurrent constituents that carry activity labels, duration spans, and numerical payloads, and then mines 'dataful' Poly-DECLARE temporal-logic clauses from those logs. The central claim is that the mined clauses, scored by whether each trace satisfies, violates, or vacuously satisfies them and then fed to a decision tree, yield an accurate classifier for MTS whose decisions are human-readable rules about temporal correlations. The authors report accuracy in the range of about 96–100% across four datasets and argue that the approach outperforms state-of-the-art MTSC classifiers, with the largest margin on a Parkinson's dyskinesia dataset where competing methods score below 26%. Because the clauses can be refined with numerical predicates learned from payloads, classes that share the same bare temporal skeleton can still be separated, which is the step the paper says makes the explanation genuinely dataful.

What carries the argument

The load-bearing object is the polyadic log model paired with the Poly-DECLARE specification language. A polyadic log is a trace collection where each timestamp can hold several durative constituents simultaneously, each marked with an activity label, a numerical payload, and a time span; this is what lets a multivariate numeric series become an event log without forcing simultaneous phenomena into one label. Poly-DECLARE extends the DECLARE templates (Init, Exists, Precedence, Response, ChainResponse, plus 'All' variants that require all constituents in an event to satisfy the condition) to test constraints on these concurrent constituents and to carry data predicates on activations and targets. The decisive mechanism is the refinement step: when the same dataless clause is mined from two class-segmented logs, a decision tree is trained on the payloads of the activating (or target) constituents, and each tree path becomes a unary predicate that specializes the clause; if no predicate separates the payloads with enough purity, the algorithm backtracks to the dataless clause. This yields class embeddings of clause satisfactions, violations, and vacuous satisfactions, over which a final decision tree produces the human-readable propositional class description.

What would settle it

Run EMeriTAte+DF on Italy Power Demand with the currently discarded high-volatility patterns included in the DT-mining step: if accuracy returns to the roughly 99.6% level of the dataless EMeriTAte while runtime stays competitive, then the pruning rule, not the dataful refinement, is what limits this dataset, and the general claim that the pipeline retains all class-discriminative information is falsified.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the separation between numerical time series analysis and event-based declarative process mining can be closed: every multivariate time series segment can be described as a polyadic trace, and Poly-DECLARE clauses mined from these traces double as both classifiers and explanations. The dataful extension attaches Catch22 summarizations to each data-trend constituent, refines shared dataless clauses into dataful ones when a decision tree can separate class payloads, and scores each trace against the resulting clause set to build an embedding for a final white-box classifier. Across the four benchmarks, this produces accuracy, precision, recall, and F1 measures that are at least comparable to strong MTSC baselines and are better than the earlier dataless EMeriTAte on three datasets out of four; on the Dyskinetic events dataset the method reaches 100% accuracy where the best competitor stays at 25.71%. The paper also shows that loading and indexing the data once, then jointly mining all class-segmented logs, makes the dataful version faster than the previous two-phase approach.

Load-bearing premise

The load-bearing premise is that the chosen discretization—pruning some high-volatility patterns and summarizing each remaining trend with Catch22 features—still contains the information that distinguishes the classes, so if the pruning removes class-relevant patterns the whole pipeline loses accuracy and the general superiority claim no longer holds.

Editorial extensions

If this is right

  • If the reported accuracy holds across settings, explainable MTS classification no longer costs accuracy: class descriptions are propositional formulas over temporal clauses, and on the Dyskinetic dataset the same model that reaches 100% accuracy explains itself purely through cross-dimension correlations.
  • Because the polyadic log model allows multiple durative events at one timestamp, temporal correlations across dimensions are expressible as DECLARE-style constraints instead of being flattened into kernel values or attention weights, which is why the paper's method succeeds where competing classifiers score below 26% on the clinical data.
  • Refining shared dataless clauses into dataful ones only when a decision tree can separate payloads by class means accuracy can only improve over the dataless variant or stay equal; on the tested datasets the refinement is always on activation conditions, never on targets.
  • Jointly mining and embedding all class-segmented logs in one pass removes the separate trace-embedding phase of the previous EMeriTAte algorithm, which is what makes the dataful version faster despite computing additional payload statistics.
  • The method also works on univariate time series (Italy Power Demand, OSULeaf), so it is not limited to cross-dimensional problems, although on Italy Power Demand it lost accuracy because discarded high-volatility patterns were class-relevant.

Reading between the lines

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

  • The pipeline is not tied to the discretization source: the polyadic log and Poly-DECLARE machinery could be applied directly to genuinely event-based data (sensor logs, process logs with concurrent activities), since the paper's own conclusion says validating on event-based data is still open.
  • The decision-tree refinement learns only unary predicates on activation or target payloads; allowing dyadic predicates that compare activation and target payloads (e.g., 'B's value must exceed A's') would likely help classes whose discrimination is relational, at the cost of the oblique-tree overhead the paper mentions.
  • The performance gap on Italy Power Demand is a natural probe: re-enabling the pruned high-volatility patterns in the mining step and re-measuring would directly test whether the pruning rule is safe in general or whether it silently drops class-specific signal.
  • Because the final explanation is a propositional formula over clauses, the same machinery yields local explanations for misclassifications: a misclassified segment can be traced to which refined clauses it violated, which is a testable use for clinical monitoring.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents EMeriTAte+DF, a pipeline for explainable multivariate time series classification. It discretizes MTS segments into polyadic logs whose durative constituents carry Catch22 payloads, mines a dataful extension of DECLARE clauses over concurrent constituents, uses clause satisfaction/violation/vacuous satisfaction to build an embedding, and trains a decision tree to obtain a white-box classifier. The authors claim that the new solution and its predecessor outperform state-of-the-art MTSC classifiers and that the approach yields human-readable explanations. Evaluation is performed on four datasets: Italy Power Demand, Basic Motions, OSULeaf, and a Parkinson's disease dyskinesia dataset.

Significance. If the accuracy and explainability claims were borne out, this would be a valuable bridge between numerical MTSC and declarative process mining. The paper contains a precise algorithmic framework (polyadic logs, taxonomies, polyadic DECLARE semantics, dataful refinement via decision trees), and it cites a public code release. The main empirical claim, however, is not supported by its own reported numbers, so the current version overstates the contribution.

major comments (4)
  1. [Abstract and §5.2, Table 4] The abstract's unqualified claim that 'our previous and current solutions outperform state-of-the-art solutions' is contradicted by the paper's own Table 4. On Italy Power Demand (Table 4a), EMeriTAte+DF scores 96.23% accuracy, below Rocket (97.02%), CIF (96.69%), STC (96.81%), and the previous EMeriTAte (99.59%). On Basic Motions (Table 4b), CIF and TapNet reach 100.0% while EMeriTAte+DF reaches 98.75%. The text in §5.2 further states that when EMeriTAte+DF did not achieve maximum scores it still achieved about 99% accuracy, which is inconsistent with the reported 96.23% on Italy Power Demand. The superiority claim must be removed or substantially qualified, and the internal inconsistency resolved.
  2. [§5.2, Fig. 4] No statistical significance tests are provided. The reported '±' values are maximum-minus-minimum ranges over 10 runs, not standard deviations or confidence intervals. Since several differences are small (e.g., 96.23% vs 96.81% on Italy Power Demand), the reader cannot determine whether EMeriTAte+DF is competitive at all. Please report standard deviations or confidence intervals and, ideally, pairwise significance tests (e.g., McNemar or a signed-rank test) against the main competitors.
  3. [§4.1 and §5.2] The pruning of 'high volatility patterns' appears to be chosen after inspecting results. Section 4.1 states that some high volatility patterns are discarded to favor shorter volatility patterns, and §5.2 attributes the Italy Power Demand shortfall to fluctuations that were not discarded in the previous solution. This makes the claimed superiority difficult to falsify: a different dataset-specific pruning could reverse the comparison. Please either justify the pruning through a validation-based procedure or clearly present it as a dataset-specific limitation, and report an ablation with and without the pruning.
  4. [Algorithm 5] Algorithm 5 uses the notation π(ς) for the span of a constituent in CHAINS (line 4) and in RESPPREC (line 29), but π is never defined in that algorithm. If π is meant to be the span function δ introduced in §3.1, this should be stated explicitly; otherwise the mining procedure is underspecified and cannot be reproduced from the pseudocode.
minor comments (5)
  1. [§4.1] There is an unresolved placeholder 'Section??' in the paragraph describing the main mining algorithm; this should be replaced with the actual section reference.
  2. [§4.1] The text refers to a 'Catch24-payload' after previously defining Catch22 features; this is presumably a typo and should be corrected.
  3. [§4.1, Lemma 2] The proof of Lemma 2 gives the bound 'O(s^2 log s C_s t/s N)', but the meaning of the term t/s is unclear and the claimed 'superpolynomial' worst-case behavior is not reconciled with the polynomial-looking expression for C_s. Please state the assumptions on C_s explicitly and give a cleaner closed-form bound.
  4. [§5.2] The statement that 'we kept different training/testing splits from the ones in our previous paper' is not accompanied by the actual split specifications; please provide the exact splits or a randomization seed for reproducibility.
  5. [Table 4] The accuracy differences between EMeriTAte+DF and competitors on Italy Power Demand are within roughly one percentage point, so the visual coloring of best/worst results should be supplemented by a discussion of effect size and variability rather than only point estimates.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pipeline's discretization, clause refinement, and classification are evaluated on held-out data, and the central construction does not reduce to its inputs.

full rationale

The derivation chain in EMeriTAte+DF is not circular. The a priori phase (Section 4.1) discretizes MTS into polyadic traces through unsupervised DT mining with Catch22 payloads; the ad hoc phase (Section 4.2) mines Poly-DECLARE clauses and refines them with decision trees trained on class labels, but the resulting embeddings and classifiers are then evaluated on held-out test splits (Section 5.2, 70%–30% stratified k-fold). No equation defines the claimed output in terms of the fitted parameters by construction, and the reported accuracies are test-set measurements rather than re-statements of training criteria. The paper does rely on the authors' prior work for algorithmic scaffolding, notably Bolt2 [9] and EMeriTAte [14], and it compares against its own previous method as a baseline; however, these self-citations are not used to forbid alternatives or to import an unverified uniqueness result, and the present paper supplies its own algorithms and experiments. Potential concerns such as hyperparameters chosen after seeing results (discarding high-volatility patterns, max depth 5) and the abstract's unqualified 'outperform state-of-the-art' claim being contradicted by Table 4 on Italy Power Demand and Basic Motions are correctness or reporting issues, not circularity. The empirical superiority claim is unsupported as stated, but the derivation itself is self-contained relative to its evaluation protocol.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces a formal data model (polyadic logs, Poly-DECLARE) but no physical or independently testable entities. Its central contributions rest on modeling assumptions about lossy discretization and on hand-set thresholds; these assumptions are the main source of fragility. The free parameters are hand-chosen or heuristic thresholds, not fitted constants, but pruning the pattern set is a model-selection choice tied to observed performance.

free parameters (5)
  • epsilon = 1e-4
    Sensitivity threshold for the pointwise predicates (increase, absence, stationarity, variability) in Table 1 and Algorithm 1; set by hand for all experiments.
  • theta = 0
    Support threshold for frequent itemsets in Algorithm 2 (Line 7); set to maximize DECLARE recall.
  • decision tree max depth = 5
    Maximum depth of white-box decision trees in Section 5.2, chosen 'to minimise the chance of generating models overfitting the data'.
  • refinement purity threshold = >50%
    Threshold for accepting a dataful refinement in Algorithm 4 Lines 25 and 37 and Algorithm 3 Line 6; no tuning procedure is given.
  • pruned data-trend pattern set = not specified
    Section 4.1 discards some high-volatility patterns to favor shorter volatility patterns; the exact set is not enumerated and appears to have been changed after observing results, per the Italy Power Demand discussion.
assumptions (4)
  • domain assumption Discretizing each dimension into pointwise predicates i, a, s, v and maximal intervals preserves enough information for classification.
    Algorithm 1 and Table 1 reduce raw numerical data to symbolic constituents before any learning; if this reduction loses class-relevant signal, all downstream clauses are built on a lossy representation.
  • domain assumption Concurrent, durative polyadic events can be treated as independent constituents within the same timestamp for DECLARE-style temporal reasoning.
    Sections 3.1-3.2 generalize DECLARE semantics by allowing several constituents per event and introduce All-variants; the assumption that co-occurrence is meaningful for the target classes is not independently validated.
  • domain assumption Decision-tree refinement of activation and target predicates with a purity threshold above 50% produces clauses that generalize from training to test traces.
    Algorithms 3-4 fit decision trees on training payloads and accept refinements only if accuracy or purity exceeds 50%; this is a heuristic with no generalization guarantee.
  • domain assumption Segmenting traces by class into maximal intervals and treating each segment as an independent trace yields a valid training distribution.
    Section 3 and Algorithm 2 split each MTS into maximum same-class segments; dependencies across segments are ignored.

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Cite this review

Pith. "Pith review of Towards Explainable Sequential Learning." pith.science (2026). https://pith.science/paper/XPQIWES5

@misc{pith2026250523624,
  author       = {Pith},
  title        = {Pith review of: Towards Explainable Sequential Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XPQIWES5}},
  note         = {Machine review of arXiv:2505.23624}
}
read the original abstract

This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology.

Figures

Figures reproduced from arXiv: 2505.23624 by the authors.

Figure 1
Figure 1. Declarative Languages for a priori explanability over (a) TS or (b) non-polyadic logs [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Fine-Grained Mining+Learning times for the training phase of both EMeriTAte [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
Figure 3
Figure 3. Comparing the cumulative training times. [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Training Results over the datasets of interest. Macro metrics are used for datasets [PITH_FULL_IMAGE:figures/full_fig_p022_4.png]

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Works this paper leans on

32 extracted references · 30 canonical work pages

  1. [1]

    van der Aalst, W.M.P.: Discovering Directly-Follows Complete Petri Nets from Event Data, pp. 539–558. Springer Nature Switzerland, Cham (2022)

    work page 2022

  2. [2]

    IEEE Comp

    Acampora, G., Vitiello, A., Di Stefano, B., van der Aalst, W., Gunther, C., Verbeek, E.: IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational In- telligence Society. IEEE Comp. Int. Mag. 12(2) (2017)

    work page 2017

  3. [3]

    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (Nov 1983)

    work page 1983

  4. [4]

    Information Systems 121, 102323 (2024),https://www.sciencedirect

    Andrzejewski, W., B˛ ebel, B., Boi´nski, P., Wrembel, R.: On tuning parameters guiding simi- larity computations in a data deduplication pipeline for customers records: Experience from a r&d project. Information Systems 121, 102323 (2024),https://www.sciencedirect. com/science/article/pii/S030643792300159X

    work page 2024

  5. [5]

    IEEE Transactions on Knowledge and Data Engineering 25(3), 556–570 (2013)

    Anselma, L., Bottrighi, A., Montani, S., Terenziani, P.: Extending bcdm to cope with proposals and evaluations of updates. IEEE Transactions on Knowledge and Data Engineering 25(3), 556–570 (2013)

    work page 2013

  6. [6]

    Scientific Reports 13(1), 15869 (Sep 2023)

    Arav, Y ., Zohar, A.: Model-based optimization of controlled release formulation of levodopa for parkinson’s disease. Scientific Reports 13(1), 15869 (Sep 2023)

    work page 2023

  7. [7]

    In: Proceedings of the International Database Engineered Appli- cations Symposium Conference, IDEAS 2023, Heraklion, Crete, Greece, May 5-7, 2023

    Avolio, M., Fuduli, A., V ocaturo, E., Zumpano, E.: On detection of diabetic retinopathy via multiple instance learning. In: Proceedings of the International Database Engineered Appli- cations Symposium Conference, IDEAS 2023, Heraklion, Crete, Greece, May 5-7, 2023. pp. 170–176. ACM (2023)

    work page 2023

  8. [8]

    Logics 2(2), 79–111 (2024)

    Bergami, G.: DECLAREd: A Polytime LTL f Fragment. Logics 2(2), 79–111 (2024)

    work page 2024

Show all 32 references
  1. [9]

    Computers 12(9) (2023)

    Bergami, G., Appleby, S., Morgan, G.: Specification mining over temporal data. Computers 12(9) (2023)

    2023

  2. [10]

    In: Proceedings of the 24th Symposium on International Database Engineering & Applications

    Bergami, G., Bertini, F., Montesi, D.: Hierarchical embedding for dag reachability queries. In: Proceedings of the 24th Symposium on International Database Engineering & Applications. IDEAS ’20, Association for Computing Machinery, New York, NY , USA (2020)

    2020

  3. [11]

    Logics 3 (2025 (To Appear))

    Bergami, G., Fox, O.R.: Extracting specifications through verified and explainable ai: Inter- pretability, interoperability, and trade-offs. Logics 3 (2025 (To Appear))

    2025

  4. [12]

    CoRR abs/2111.12454 (2021)

    Bergami, G., Francescomarino, C.D., Ghidini, C., Maggi, F.M., Puura, J.: Exploring business process deviance with sequential and declarative patterns. CoRR abs/2111.12454 (2021)

    2021 arXiv

  5. [13]

    In: 2021 3rd International Conference on Process Mining (ICPM)

    Bergami, G., Maggi, F.M., Montali, M., Peñaloza, R.: Probabilistic trace alignment. In: 2021 3rd International Conference on Process Mining (ICPM). pp. 9–16 (2021)

    2021

  6. [14]

    In: Database Engineered Applications

    Bergami, G., Packer, E., Scott, K., Del Din, S.: Predicting dyskinetic events through verified multivariate time series classification. In: Database Engineered Applications. IDEAS ’24 (in press), Springer (2025)

    2025

  7. [15]

    In: AAAI’17

    De Giacomo, G., Maggi, F.M., Marrella, A., Patrizi, F.: On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment. In: AAAI’17. AAAI press (2017)

    2017

  8. [16]

    Data Min

    Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Discov. 34(5), 1454–1495 (2020)

    2020

  9. [17]

    In: Runtime Verification - 4th International Conference, RV 2013, Rennes, France, September 24-27, 2013

    Donzé, A.: On signal temporal logic. In: Runtime Verification - 4th International Conference, RV 2013, Rennes, France, September 24-27, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8174, pp. 382–383. Springer (2013)

    2013

  10. [18]

    Master’s thesis, Ore- gon State University (2002) Towards Explainable Sequential Learning 25

    Gandhi, A.: Content-Based Image Retrieval: Plant Species Identification. Master’s thesis, Ore- gon State University (2002) Towards Explainable Sequential Learning 25

    2002

  11. [19]

    Data Mining and Knowledge Discovery 28(4), 851–881 (Jul 2014)

    Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery 28(4), 851–881 (Jul 2014)

    2014

  12. [20]

    Expert Systems with Applica- tions 201, 117176 (2022)

    Huo, X., Hao, K., Chen, L., song Tang, X., Wang, T., Cai, X.: A dynamic soft sensor of indus- trial fuzzy time series with propositional linear temporal logic. Expert Systems with Applica- tions 201, 117176 (2022)

    2022

  13. [21]

    Communications Engineering 3(1), 10 (Jan 2024),https://doi.org/10.1038/ s44172-023-00158-0

    Inoue, T., Kubota, K., Ikami, T., Egami, Y ., Nagai, H., Kashikawa, T., Kimura, K., Matsuda, Y .: Clustering method for time-series images using quantum-inspired digital annealer technol- ogy. Communications Engineering 3(1), 10 (Jan 2024),https://doi.org/10.1038/ s44172-023-00158-0

    2024

  14. [22]

    In: Sixth International Conference on Data Mining (ICDM’06)

    Keogh, E., Wei, L., Xi, X., Lonardi, S., Shieh, J., Sirowy, S.: Intelligent icons: Integrating lite-weight data mining and visualization into gui operating systems. In: Sixth International Conference on Data Mining (ICDM’06). pp. 912–916 (2006)

    2006

  15. [23]

    Information Systems 89, 101482 (2020)

    Leno, V ., Dumas, M., Maggi, F.M., La Rosa, M., Polyvyanyy, A.: Automated discovery of declarative process models with correlated data conditions. Information Systems 89, 101482 (2020)

    2020

  16. [24]

    CoRR abs/1909.07872 (2019),http: //arxiv.org/abs/1909.07872

    Löning, M., Bagnall, A.J., Ganesh, S., Kazakov, V ., Lines, J., Király, F.J.: sktime: A uni- fied interface for machine learning with time series. CoRR abs/1909.07872 (2019),http: //arxiv.org/abs/1909.07872

    2019 arXiv

  17. [25]

    Data Min

    Lubba, C.H., Sethi, S.S., Knaute, P., Schultz, S.R., Fulcher, B.D., Jones, N.S.: catch22: Canon- ical time-series characteristics - selected through highly comparative time-series analysis. Data Min. Knowl. Discov. 33(6), 1821–1852 (2019)

    2019

  18. [26]

    In: 2020 IEEE International Conference on Big Data (Big Data)

    Middlehurst, M., Large, J., Bagnall, A.: The canonical interval forest (cif) classifier for time series classification. In: 2020 IEEE International Conference on Big Data (Big Data). pp. 188– 195 (Dec 2020)

    2020

  19. [27]

    In: EDOC

    Peši ´c, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely- structured processes. In: EDOC. pp. 287–300. IEEE Computer Society (2007)

    2007

  20. [28]

    Rost, C., Gómez, K., Täschner, M., Fritzsche, P., Schons, L., Christ, L., Adameit, T., Junghanns, M., Rahm, E.: Distributed temporal graph analytics with GRADOOP. VLDB J. 31(2), 375–401 (2022)

    2022

  21. [29]

    Data Mining and Knowledge Discovery 35(2), 401–449 (Mar 2021)

    Ruiz, A.P., Flynn, M., Large, J., Middlehurst, M., Bagnall, A.: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery 35(2), 401–449 (Mar 2021)

    2021

  22. [30]

    Seshia, S.A., Sadigh, D., Sastry, S.S.: Toward verified artificial intelligence. Commun. ACM 65(7), 46–55 (Jun 2022),https://doi.org/10.1145/3503914

    2022 doi

  23. [31]

    Proceedings of the AAAI Conference on Artificial Intelligence 33(01), 5621–5627 (Jul 2019),https://ojs.aaai

    Yang, B.B., Shen, S.Q., Gao, W.: Weighted oblique decision trees. Proceedings of the AAAI Conference on Artificial Intelligence 33(01), 5621–5627 (Jul 2019),https://ojs.aaai. org/index.php/AAAI/article/view/4505

    2019

  24. [32]

    In: AAAI

    Zhang, X., Gao, Y ., Lin, J., Lu, C.: TapNet: Multivariate time series classification with atten- tional prototypical network. In: AAAI. pp. 6845–6852. AAAI Press (2020)

    2020

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Reviewed August 7, 2026 · model on record in the stance chip above.