{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/time-series-classification/papers/4","list_of":"/task/time-series-classification","task":"Time Series Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":7,"rows_per_page":100,"rows":[301,400],"of":697,"counts":{"archive_papers_tagged":697,"with_a_code_link":328,"where_syntology_ran_a_sample":68,"not_listed_spam_title":0,"listed":697,"listed_where_code_ran":68,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":60,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":60,"listed_every_run_a_failure_of_syntologys_instrument":8,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/time-series-classification","prev":"/task/time-series-classification/papers/3","next":"/task/time-series-classification/papers/5","papers":[{"url":"/paper/scalable-dictionary-classifiers-for-time","slug":"scalable-dictionary-classifiers-for-time","title":"Scalable Dictionary Classifiers for Time Series Classification","date":"2019-07-26","arxiv_id":"1907.11815","repositories_listed":1,"syntology":null},{"url":"/paper/a-1d-convolutional-network-for-leaf-and-time","slug":"a-1d-convolutional-network-for-leaf-and-time","title":"A 1d convolutional network for leaf and time series classification","date":"2019-06-28","arxiv_id":"1907.00069","repositories_listed":1,"syntology":null},{"url":"/paper/gesture-recognition-in-rgb-videos-usinghuman","slug":"gesture-recognition-in-rgb-videos-usinghuman","title":"Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time Warping","date":"2019-06-25","arxiv_id":"1906.12171","repositories_listed":1,"syntology":null},{"url":"/paper/variational-gaussian-processes-with-signature","slug":"variational-gaussian-processes-with-signature","title":"Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances","date":"2019-06-19","arxiv_id":"1906.08215","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/variational-gaussian-processes-with-signature#ran","syntology_url":"https://syntology.ai/paper/1906.08215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.08215"}},"official":{"repos":["tgcsaba/GPSig"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-transformer-networks-joint-learning-1","slug":"temporal-transformer-networks-joint-learning-1","title":"Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping","date":"2019-06-13","arxiv_id":"1906.05947","repositories_listed":1,"syntology":null},{"url":"/paper/multivariate-time-series-classification-using-1","slug":"multivariate-time-series-classification-using-1","title":"Multivariate Time Series Classification using Dilated Convolutional Neural Network","date":"2019-05-05","arxiv_id":"1905.01697","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-attention-augmented-bilinear-network-1","slug":"temporal-attention-augmented-bilinear-network-1","title":"Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis","date":"2019-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-attacks-on-deep-neural-networks","slug":"adversarial-attacks-on-deep-neural-networks","title":"Adversarial Attacks on Deep Neural Networks for Time Series Classification","date":"2019-03-17","arxiv_id":"1903.07054","repositories_listed":1,"syntology":null},{"url":"/paper/sinkhorn-divergence-of-topological-signature","slug":"sinkhorn-divergence-of-topological-signature","title":"Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification","date":"2019-02-14","arxiv_id":"1902.05326","repositories_listed":1,"syntology":null},{"url":"/paper/waveletfcnn-a-deep-time-series-classification","slug":"waveletfcnn-a-deep-time-series-classification","title":"WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection","date":"2019-02-14","arxiv_id":"1902.05625","repositories_listed":1,"syntology":null},{"url":"/paper/fastgrnn-a-fast-accurate-stable-and-tiny","slug":"fastgrnn-a-fast-accurate-stable-and-tiny","title":"FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network","date":"2019-01-08","arxiv_id":"1901.02358","repositories_listed":1,"syntology":null},{"url":"/paper/deep-gated-recurrent-and-convolutional","slug":"deep-gated-recurrent-and-convolutional","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","date":"2018-12-18","arxiv_id":"1812.07683","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-relationships-by-multi-domain","slug":"extracting-relationships-by-multi-domain","title":"Extracting Relationships by Multi-Domain Matching","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-filter-widths-of-spectral","slug":"learning-filter-widths-of-spectral","title":"Learning filter widths of spectral decompositions with wavelets","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multiple-instance-learning-for-efficient","slug":"multiple-instance-learning-for-efficient","title":"Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-for-time-series","slug":"transfer-learning-for-time-series","title":"Transfer learning for time series classification","date":"2018-11-05","arxiv_id":"1811.01533","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-time-series-tweaking-via","slug":"explainable-time-series-tweaking-via","title":"Explainable time series tweaking via irreversible and reversible temporal transformations","date":"2018-09-13","arxiv_id":"1809.05183","repositories_listed":1,"syntology":null},{"url":"/paper/elastic-bands-across-the-path-a-new-framework","slug":"elastic-bands-across-the-path-a-new-framework","title":"Elastic bands across the path: A new framework and methods to lower bound DTW","date":"2018-08-29","arxiv_id":"1808.09617","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-time-series-classification","slug":"interpretable-time-series-classification","title":"Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains","date":"2018-08-12","arxiv_id":"1808.04022","repositories_listed":1,"syntology":null},{"url":"/paper/multivariate-time-series-classification-with","slug":"multivariate-time-series-classification-with","title":"Multivariate Time Series Classification with WEASEL+MUSE","date":"2017-11-30","arxiv_id":"1711.11343","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-time-series-images-using","slug":"classification-of-time-series-images-using","title":"Classification of Time-Series Images Using Deep Convolutional Neural Networks","date":"2017-10-02","arxiv_id":"1710.00886","repositories_listed":1,"syntology":null},{"url":"/paper/encase-an-ensemble-classifier-for-ecg","slug":"encase-an-ensemble-classifier-for-ecg","title":"ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks","date":"2017-09-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/time-series-data-cleaning-from-anomaly","slug":"time-series-data-cleaning-from-anomaly","title":"Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing","date":"2017-06-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fast-and-accurate-time-series-classification","slug":"fast-and-accurate-time-series-classification","title":"Fast and Accurate Time Series Classification with WEASEL","date":"2017-01-26","arxiv_id":"1701.07681","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-with-deconvolution","slug":"representation-learning-with-deconvolution","title":"Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization","date":"2016-10-24","arxiv_id":"1610.07258","repositories_listed":1,"syntology":null},{"url":"/paper/a-scalable-end-to-end-gaussian-process","slug":"a-scalable-end-to-end-gaussian-process","title":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","date":"2016-06-14","arxiv_id":"1606.04443","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-scalable-end-to-end-gaussian-process#ran","syntology_url":"https://syntology.ai/paper/1606.04443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.04443"}},"official":null}},{"url":"/paper/support-vector-machines-with-time-series","slug":"support-vector-machines-with-time-series","title":"Support Vector Machines with Time Series Distance Kernels for Action Classification","date":"2016-03-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/highly-comparative-time-series-analysis-the","slug":"highly-comparative-time-series-analysis-the","title":"Highly comparative time-series analysis: The empirical structure of time series and their methods","date":"2013-04-03","arxiv_id":"1304.1209","repositories_listed":1,"syntology":null},{"url":null,"slug":"stact-time-spatio-temporal-cross-attention","title":"STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification","date":"2025-06-22","arxiv_id":"2506.18172","repositories_listed":0,"syntology":null},{"url":null,"slug":"moric-csi-delay-doppler-decomposition-for","title":"MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition","date":"2025-06-15","arxiv_id":"2506.12997","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-representations-for","title":"Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers","date":"2025-06-10","arxiv_id":"2506.08641","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-imposed-fusion-a-simple-yet-effective","title":"Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification","date":"2025-05-31","arxiv_id":"2506.00337","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-images-to-signals-are-large-vision","title":"From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?","date":"2025-05-29","arxiv_id":"2505.24030","repositories_listed":0,"syntology":null},{"url":null,"slug":"qsvm-qnn-quantum-support-vector-machine-based","title":"QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems","date":"2025-05-20","arxiv_id":"2505.14192","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-simplification-algorithms-for","title":"Evaluating Simplification Algorithms for Interpretability of Time Series Classification","date":"2025-05-13","arxiv_id":"2505.08846","repositories_listed":0,"syntology":null},{"url":"/paper/learning-advanced-self-attention-for-linear","slug":"learning-advanced-self-attention-for-linear","title":"Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain","date":"2025-05-13","arxiv_id":"2505.08516","repositories_listed":0,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-advanced-self-attention-for-linear#ran","syntology_url":"https://syntology.ai/paper/2505.08516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.08516"}},"official":null}},{"url":null,"slug":"fic-tsc-learning-time-series-classification","title":"FIC-TSC: Learning Time Series Classification with Fisher Information Constraint","date":"2025-05-09","arxiv_id":"2505.06114","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-transport-based-dissimilarity","title":"An Efficient Transport-Based Dissimilarity Measure for Time Series Classification under Warping Distortions","date":"2025-05-08","arxiv_id":"2505.05676","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligently-augmented-contrastive-tensor","title":"Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments","date":"2025-05-03","arxiv_id":"2505.03825","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-multivariate-financial-time-series","title":"On Multivariate Financial Time Series Classification","date":"2025-04-24","arxiv_id":"2504.17664","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-compositional-transferability-of","title":"Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation","date":"2025-04-21","arxiv_id":"2504.14994","repositories_listed":0,"syntology":null},{"url":null,"slug":"alt-a-python-package-for-lightweight-feature","title":"ALT: A Python Package for Lightweight Feature Representation in Time Series Classification","date":"2025-04-17","arxiv_id":"2504.12841","repositories_listed":0,"syntology":null},{"url":null,"slug":"identiarat-toward-automated-identification-of","title":"IdentiARAT: Toward Automated Identification of Individual ARAT Items from Wearable Sensors","date":"2025-04-17","arxiv_id":"2504.12921","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardware-friendly-delayed-feedback-reservoir","title":"Hardware-Friendly Delayed-Feedback Reservoir for Multivariate Time-Series Classification","date":"2025-04-16","arxiv_id":"2504.11981","repositories_listed":0,"syntology":null},{"url":null,"slug":"aimts-augmented-series-and-image-contrastive","title":"AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification","date":"2025-04-14","arxiv_id":"2504.09993","repositories_listed":0,"syntology":null},{"url":null,"slug":"interval-valued-time-series-classification","title":"Interval-Valued Time Series Classification Using $D_K$-Distance","date":"2025-04-07","arxiv_id":"2504.04667","repositories_listed":0,"syntology":null},{"url":null,"slug":"cats-mitigating-correlation-shift-for","title":"CATS: Mitigating Correlation Shift for Multivariate Time Series Classification","date":"2025-04-05","arxiv_id":"2504.04283","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-classification-of-interval-valued","title":"Adaptive Classification of Interval-Valued Time Series","date":"2025-04-04","arxiv_id":"2504.03318","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparseformer-a-transferable-transformer-with","title":"Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification","date":"2025-03-19","arxiv_id":"2503.15578","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-backdoor-attacks-on-time-series","title":"Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain","date":"2025-03-12","arxiv_id":"2503.09712","repositories_listed":0,"syntology":null},{"url":null,"slug":"relate-resilient-learner-selection-for","title":"ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks","date":"2025-03-10","arxiv_id":"2503.07882","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardware-accelerated-event-graph-neural","title":"Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA","date":"2025-03-09","arxiv_id":"2503.06629","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-projections-and-natural-sparsity-in","title":"Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis","date":"2025-02-24","arxiv_id":"2502.17061","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-transformer-inference","title":"Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification","date":"2025-02-23","arxiv_id":"2502.16627","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-and-class-revealing-signal","title":"Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization","date":"2025-02-08","arxiv_id":"2502.05722","repositories_listed":0,"syntology":null},{"url":null,"slug":"trojantime-backdoor-attacks-on-time-series","title":"TrojanTime: Backdoor Attacks on Time Series Classification","date":"2025-02-02","arxiv_id":"2502.00646","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-are-few-shot-1","title":"Large Language Models are Few-shot Multivariate Time Series Classifiers","date":"2025-01-30","arxiv_id":"2502.00059","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-1-d-cnn-inference-engine-for-constrained","title":"A 1-D CNN inference engine for constrained platforms","date":"2025-01-28","arxiv_id":"2501.17269","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-disease-outbreak-prediction","title":"Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations","date":"2025-01-14","arxiv_id":"2501.07764","repositories_listed":0,"syntology":null},{"url":null,"slug":"vsformer-value-and-shape-aware-transformer","title":"VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification","date":"2024-12-21","arxiv_id":"2412.16515","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-aware-balanced-spectrum-encoding-in","title":"Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification","date":"2024-12-17","arxiv_id":"2412.13232","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-risk-control-via-prediction","title":"Semi-Supervised Risk Control via Prediction-Powered Inference","date":"2024-12-15","arxiv_id":"2412.11174","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-pre-training-data-quality-without","title":"Measuring Pre-training Data Quality without Labels for Time Series Foundation Models","date":"2024-12-09","arxiv_id":"2412.06368","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-abba-understand-time-series-via-symbolic","title":"LLM-ABBA: Understanding time series via symbolic approximation","date":"2024-11-27","arxiv_id":"2411.18506","repositories_listed":0,"syntology":null},{"url":null,"slug":"st-tree-with-interpretability-for","title":"ST-Tree with Interpretability for Multivariate Time Series Classification","date":"2024-11-18","arxiv_id":"2411.11620","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-literature-review-of-spatio","title":"A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification","date":"2024-10-29","arxiv_id":"2410.22377","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-streaming-batch-principal-component","title":"Temporal Streaming Batch Principal Component Analysis for Time Series Classification","date":"2024-10-28","arxiv_id":"2410.20820","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-multimodal-llms-with-semantic","title":"Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification","date":"2024-10-24","arxiv_id":"2410.18686","repositories_listed":0,"syntology":null},{"url":null,"slug":"ts-acl-a-time-series-analytic-continual","title":"TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning","date":"2024-10-21","arxiv_id":"2410.15954","repositories_listed":0,"syntology":null},{"url":null,"slug":"abba-vsm-time-series-classification-using","title":"ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge","date":"2024-10-14","arxiv_id":"2410.10285","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-alchemist-an-innovative-fully","title":"Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification","date":"2024-10-12","arxiv_id":"2410.09307","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-sparse-sampling-a-framework-for","title":"Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification","date":"2024-10-08","arxiv_id":"2410.06412","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-classification-of-supraglacial","title":"Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet","date":"2024-10-08","arxiv_id":"2410.05638","repositories_listed":0,"syntology":null},{"url":null,"slug":"forest-proximities-for-time-series","title":"Forest Proximities for Time Series","date":"2024-10-04","arxiv_id":"2410.03098","repositories_listed":0,"syntology":null},{"url":null,"slug":"repurposing-foundation-model-for","title":"Repurposing Foundation Model for Generalizable Medical Time Series Classification","date":"2024-10-03","arxiv_id":"2410.03794","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-representation-learning-for-4","title":"Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data","date":"2024-10-01","arxiv_id":"2410.00312","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-tensor-decomposition-for-time-series","title":"Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast","date":"2024-09-23","arxiv_id":"2409.15200","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-friendly-foundation-model-adapters-for","title":"User-friendly Foundation Model Adapters for Multivariate Time Series Classification","date":"2024-09-18","arxiv_id":"2409.12264","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-tinyml-the-impact-of-reduced-data","title":"Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers","date":"2024-09-17","arxiv_id":"2409.10942","repositories_listed":0,"syntology":null},{"url":null,"slug":"tx-gen-multi-objective-optimization-for","title":"TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification","date":"2024-09-14","arxiv_id":"2409.09461","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-spline-trees-for-functional-data","title":"Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series","date":"2024-09-12","arxiv_id":"2409.07879","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-certificate-robustness-for-time","title":"Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble","date":"2024-09-04","arxiv_id":"2409.02802","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-time-series-classification-with","title":"Improving Time Series Classification with Representation Soft Label Smoothing","date":"2024-08-30","arxiv_id":"2408.17010","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-counterfactual-interpretability","title":"Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification","date":"2024-08-22","arxiv_id":"2408.12666","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlation-analysis-of-adversarial-attack-in","title":"Correlation Analysis of Adversarial Attack in Time Series Classification","date":"2024-08-21","arxiv_id":"2408.11264","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-the-deepdream-for-time-series","title":"Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series","date":"2024-08-20","arxiv_id":"2408.10628","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-counterfactual-generation-for","title":"Interactive Counterfactual Generation for Univariate Time Series","date":"2024-08-20","arxiv_id":"2408.10633","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-model-for-time-series","title":"An End-to-End Model for Time Series Classification In the Presence of Missing Values","date":"2024-08-11","arxiv_id":"2408.05849","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-02835","title":"Training a multilayer dynamical spintronic network with standard machine learning tools to perform time series classification","date":"2024-08-05","arxiv_id":"2408.02835","repositories_listed":0,"syntology":null},{"url":null,"slug":"con4m-context-aware-consistency-learning","title":"Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification","date":"2024-07-31","arxiv_id":"2408.00041","repositories_listed":0,"syntology":null},{"url":null,"slug":"quadratic-advantage-with-quantum-randomized","title":"Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis","date":"2024-07-25","arxiv_id":"2407.18021","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiervar-a-hierarchical-feature-selection","title":"HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis","date":"2024-07-22","arxiv_id":"2407.16048","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecrtime-ensemble-integration-of","title":"ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classification","date":"2024-07-20","arxiv_id":"2407.14735","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinear-schrodinger-network","title":"Physical Data Embedding for Memory Efficient AI","date":"2024-07-19","arxiv_id":"2407.14504","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-facial-biomarkers-for-depression","title":"Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units","date":"2024-07-18","arxiv_id":"2407.13753","repositories_listed":0,"syntology":null},{"url":null,"slug":"lets-c-leveraging-language-embedding-for-time","title":"LETS-C: Leveraging Text Embedding for Time Series Classification","date":"2024-07-09","arxiv_id":"2407.06533","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-photoplethysmography-to-detect-real","title":"Using Photoplethysmography to Detect Real-time Blood Pressure Changes with a Calibration-free Deep Learning Model","date":"2024-07-03","arxiv_id":"2407.03274","repositories_listed":0,"syntology":null},{"url":null,"slug":"capturing-temporal-components-for-time-series","title":"Capturing Temporal Components for Time Series Classification","date":"2024-06-20","arxiv_id":"2406.14456","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-primal-dual-framework-for-transformers-and","title":"A Primal-Dual Framework for Transformers and Neural Networks","date":"2024-06-19","arxiv_id":"2406.13781","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-when-is-reservoir-computing-with-cellular","title":"On when is Reservoir Computing with Cellular Automata Beneficial?","date":"2024-06-13","arxiv_id":"2407.09501","repositories_listed":0,"syntology":null}],"record_sha256":"5a74c1bba2e50db15149b71e67a8dd16649d774b82964581a263f18370d37d52","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}