{"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/5","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":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"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/4","next":"/task/time-series-classification/papers/6","papers":[{"url":null,"slug":"data-augmentation-for-multivariate-time","title":"Data Augmentation for Multivariate Time Series Classification: An Experimental Study","date":"2024-06-10","arxiv_id":"2406.06518","repositories_listed":0,"syntology":null},{"url":null,"slug":"chimera-effectively-modeling-multivariate","title":"Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models","date":"2024-06-06","arxiv_id":"2406.04320","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-elastic-neural-networks","title":"Time Elastic Neural Networks","date":"2024-05-27","arxiv_id":"2405.17516","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-accurate-ego-lane-identification-with","title":"Towards Accurate Ego-lane Identification with Early Time Series Classification","date":"2024-05-27","arxiv_id":"2405.17270","repositories_listed":0,"syntology":null},{"url":null,"slug":"shapeformer-shapelet-transformer-for","title":"ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification","date":"2024-05-23","arxiv_id":"2405.14608","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-learning-framework-with-a-class","title":"An Active Learning Framework with a Class Balancing Strategy for Time Series Classification","date":"2024-05-20","arxiv_id":"2405.12122","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-transformers-using-faithful","title":"Improving Transformers using Faithful Positional Encoding","date":"2024-05-15","arxiv_id":"2405.09061","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-explainable-ai-method-grad-cam","title":"Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data","date":"2024-05-13","arxiv_id":"2405.07590","repositories_listed":0,"syntology":null},{"url":null,"slug":"adafsnet-time-series-classification-based-on","title":"AdaFSNet: Time Series Classification Based on Convolutional Network with a Adaptive and Effective Kernel Size Configuration","date":"2024-04-28","arxiv_id":"2404.18246","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-rocket-and-catch22-features-for","title":"Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models","date":"2024-04-28","arxiv_id":"2404.18159","repositories_listed":0,"syntology":null},{"url":null,"slug":"finlangnet-a-novel-deep-learning-framework","title":"A Generative Approach to Credit Prediction with Learnable Prompts for Multi-scale Temporal Representation Learning","date":"2024-04-19","arxiv_id":"2404.13004","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-detection-of-disease-outbreaks-and-non","title":"Early detection of disease outbreaks and non-outbreaks using incidence data","date":"2024-04-13","arxiv_id":"2404.08893","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-eeg-sequences-time-series-eeg","title":"Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training","date":"2024-04-10","arxiv_id":"2404.06966","repositories_listed":0,"syntology":null},{"url":null,"slug":"timecsl-unsupervised-contrastive-learning-of","title":"TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis","date":"2024-04-07","arxiv_id":"2404.05057","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepheteroiot-deep-local-and-global-learning","title":"DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data","date":"2024-03-29","arxiv_id":"2403.19996","repositories_listed":0,"syntology":null},{"url":null,"slug":"inceptiontime-vs-wavelet-a-comparison-for","title":"InceptionTime vs. Wavelet -- A comparison for time series classification","date":"2024-03-27","arxiv_id":"2403.18687","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-physical-fitness-monitoring-pfm","title":"Multimodal Physical Fitness Monitoring (PFM) Framework Based on TimeMAE-PFM in Wearable Scenarios","date":"2024-03-25","arxiv_id":"2404.15294","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-time-series-classification-for","title":"Supervised Time Series Classification for Anomaly Detection in Subsea Engineering","date":"2024-03-12","arxiv_id":"2403.08013","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-high-resolution-time-series","title":"Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition","date":"2024-03-07","arxiv_id":"2403.04882","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-uav-type-an-exploration-of","title":"Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification","date":"2024-03-01","arxiv_id":"2403.00565","repositories_listed":0,"syntology":null},{"url":null,"slug":"dtcm-deep-transformer-capsule-mutual","title":"DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification","date":"2024-02-26","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mstar-multi-scale-backbone-architecture","title":"MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification","date":"2024-02-21","arxiv_id":"2402.13822","repositories_listed":0,"syntology":null},{"url":null,"slug":"smore-similarity-based-hyperdimensional","title":"SMORE: Similarity-based Hyperdimensional Domain Adaptation for Multi-Sensor Time Series Classification","date":"2024-02-20","arxiv_id":"2402.13233","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-financially-inclusive-credit-products","title":"Towards Financially Inclusive Credit Products Through Financial Time Series Clustering","date":"2024-02-16","arxiv_id":"2402.11066","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sequential-model-performance-with","title":"Enhancing Sequential Model Performance with Squared Sigmoid TanH (SST) Activation Under Data Constraints","date":"2024-02-14","arxiv_id":"2402.09034","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-classification-for-dynamic","title":"Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting","date":"2024-02-13","arxiv_id":"2402.08373","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-time-series-under-temporal","title":"Learning under Temporal Label Noise","date":"2024-02-06","arxiv_id":"2402.04398","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-driven-domain-generalizable-learning","title":"Phase-driven Domain Generalizable Learning for Nonstationary Time Series","date":"2024-02-05","arxiv_id":"2402.05960","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-time-series-anomaly-state","title":"Understanding Time Series Anomaly State Detection through One-Class Classification","date":"2024-02-03","arxiv_id":"2402.02007","repositories_listed":0,"syntology":null},{"url":null,"slug":"shapelet-based-model-agnostic-counterfactual","title":"Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification","date":"2024-02-02","arxiv_id":"2402.01343","repositories_listed":0,"syntology":null},{"url":null,"slug":"rank-supervised-contrastive-learning-for-time","title":"Rank Supervised Contrastive Learning for Time Series Classification","date":"2024-01-31","arxiv_id":"2401.18057","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-volatile-organic-compounds","title":"Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves","date":"2024-01-13","arxiv_id":"2401.07066","repositories_listed":0,"syntology":null},{"url":null,"slug":"operator-learning-inspired-modeling-of-neural","title":"Operator-learning-inspired Modeling of Neural Ordinary Differential Equations","date":"2023-12-16","arxiv_id":"2312.10274","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-dag-convolutional-networks","title":"Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification","date":"2023-12-16","arxiv_id":"2312.10317","repositories_listed":0,"syntology":null},{"url":null,"slug":"chemtime-rapid-and-early-classification-for","title":"ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors","date":"2023-12-15","arxiv_id":"2312.09871","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-machine-learning-based","title":"Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management","date":"2023-12-11","arxiv_id":"2312.06854","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-classification-of-large-time-series","title":"Fast Classification of Large Time Series Datasets","date":"2023-12-10","arxiv_id":"2312.06029","repositories_listed":0,"syntology":null},{"url":"/paper/virtual-fusion-with-contrastive-learning-for","slug":"virtual-fusion-with-contrastive-learning-for","title":"Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition","date":"2023-12-01","arxiv_id":"2312.02185","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bag-of-receptive-fields-for-time-series","title":"A Bag of Receptive Fields for Time Series Extrinsic Predictions","date":"2023-11-29","arxiv_id":"2311.18029","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-time-series-classification-3","title":"Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data","date":"2023-11-28","arxiv_id":"2311.16381","repositories_listed":0,"syntology":null},{"url":null,"slug":"xai-for-time-series-classification-leveraging","title":"XAI for time-series classification leveraging image highlight methods","date":"2023-11-28","arxiv_id":"2311.17110","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-performance-of-heart-rate-time","title":"Improving performance of heart rate time series classification by grouping subjects","date":"2023-11-22","arxiv_id":"2311.13285","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-transformation-for-iot-time-series-data","title":"Image Transformation for IoT Time-Series Data: A Review","date":"2023-11-21","arxiv_id":"2311.12742","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-using-data-augmentation-and","title":"Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification","date":"2023-11-06","arxiv_id":"2311.03194","repositories_listed":0,"syntology":null},{"url":null,"slug":"ego-network-transformer-for-subsequence","title":"Ego-Network Transformer for Subsequence Classification in Time Series Data","date":"2023-11-05","arxiv_id":"2311.02561","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-treasure-hunt-content-based-time","title":"Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights","date":"2023-11-05","arxiv_id":"2311.02560","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-time-series-anomaly-detection-with","title":"Few-shot time-series anomaly detection with unsupervised domain adaptation","date":"2023-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-user-behaviors-make-the-differences","title":"What User Behaviors Make the Differences During the Process of Visual Analytics?","date":"2023-11-01","arxiv_id":"2311.00690","repositories_listed":0,"syntology":null},{"url":null,"slug":"micronas-memory-and-latency-constrained","title":"MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers","date":"2023-10-27","arxiv_id":"2310.18384","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-iot-based-asset-and-utilization","title":"Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices","date":"2023-10-23","arxiv_id":"2310.14758","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-time-series","title":"Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey","date":"2023-10-16","arxiv_id":"2310.10060","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-processes-based-data-augmentation","title":"Gaussian processes based data augmentation and expected signature for time series classification","date":"2023-10-16","arxiv_id":"2310.10836","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-end-to-end-contrastive","title":"Semi-Supervised End-To-End Contrastive Learning For Time Series Classification","date":"2023-10-13","arxiv_id":"2310.08848","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-enhanced-forecasting-leveraging","title":"Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions","date":"2023-10-11","arxiv_id":"2310.07427","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-learning-for-time-series-data-with","title":"Multitask Learning for Time Series Data with 2D Convolution","date":"2023-10-05","arxiv_id":"2310.03925","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-classification-in-smart","title":"Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms","date":"2023-10-04","arxiv_id":"2310.02812","repositories_listed":0,"syntology":null},{"url":null,"slug":"sourcing-investment-targets-for-venture-and","title":"Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems","date":"2023-09-28","arxiv_id":"2309.16888","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffeomorphic-transformations-for-time-series","title":"Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping","date":"2023-09-25","arxiv_id":"2309.14029","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-churn-prediction-from-large-scale-user","title":"Early Churn Prediction from Large Scale User-Product Interaction Time Series","date":"2023-09-25","arxiv_id":"2309.14390","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-hierarchical-structures-for","title":"Generating Hierarchical Structures for Improved Time Series Classification Using Stochastic Splitting Functions","date":"2023-09-21","arxiv_id":"2309.11963","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-pre-training-of-time-series-data","title":"Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing","date":"2023-09-20","arxiv_id":"2309.11427","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcns-mining-causal-natural-structures-inside","title":"MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme","date":"2023-09-13","arxiv_id":"2309.06739","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-the-effect-of-pre-training-on-time","title":"Examining the Effect of Pre-training on Time Series Classification","date":"2023-09-11","arxiv_id":"2309.05256","repositories_listed":0,"syntology":null},{"url":null,"slug":"swap-exploiting-second-ranked-logits-for","title":"SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series","date":"2023-09-06","arxiv_id":"2309.02752","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-time-series-classification-with-2","title":"Multivariate time series classification with dual attention network","date":"2023-08-26","arxiv_id":"2308.13968","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-positioning-using","title":"Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments","date":"2023-08-22","arxiv_id":"2308.11670","repositories_listed":0,"syntology":null},{"url":null,"slug":"domino-domain-invariant-hyperdimensional","title":"DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data","date":"2023-08-07","arxiv_id":"2308.03295","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-feature-engineering-for-time-series","title":"Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion","date":"2023-08-02","arxiv_id":"2308.01071","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-explanations-with-attributions-and","title":"Visual Explanations with Attributions and Counterfactuals on Time Series Classification","date":"2023-07-14","arxiv_id":"2307.08494","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-time-series-classification-a","title":"Multivariate Time Series Classification: A Deep Learning Approach","date":"2023-07-05","arxiv_id":"2307.02253","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-time-warping-invariant-dictionary","title":"Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering","date":"2023-06-30","arxiv_id":"2306.17690","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-motif-based-time-series","title":"Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids","date":"2023-06-23","arxiv_id":"2306.13397","repositories_listed":0,"syntology":null},{"url":null,"slug":"mts2graph-interpretable-multivariate-time","title":"MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs","date":"2023-06-06","arxiv_id":"2306.03834","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagnostic-spatio-temporal-transformer-with","title":"Diagnostic Spatio-temporal Transformer with Faithful Encoding","date":"2023-05-26","arxiv_id":"2305.17149","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-extraction-with-interval-temporal","title":"Knowledge Extraction with Interval Temporal Logic Decision Trees","date":"2023-05-26","arxiv_id":"2305.16864","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dictionary-based-approach-to-time-series","title":"A Dictionary-based approach to Time Series Ordinal Classification","date":"2023-05-16","arxiv_id":"2305.09288","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ensemble-of-convolution-based-methods-for","title":"An ensemble of convolution-based methods for fault detection using vibration signals","date":"2023-05-05","arxiv_id":"2305.05532","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-activity-representation","title":"Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study","date":"2023-05-01","arxiv_id":"2305.00619","repositories_listed":0,"syntology":null},{"url":null,"slug":"embarrassingly-simple-mixup-for-time-series","title":"Embarrassingly Simple MixUp for Time-series","date":"2023-04-09","arxiv_id":"2304.04271","repositories_listed":0,"syntology":null},{"url":null,"slug":"never-a-dull-moment-distributional-properties","title":"Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification","date":"2023-03-31","arxiv_id":"2303.17809","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-private-to-public-benchmarking-gans-in","title":"From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification","date":"2023-03-28","arxiv_id":"2303.15916","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ai-for-time-series-via-virtual","title":"Explainable AI for Time Series via Virtual Inspection Layers","date":"2023-03-11","arxiv_id":"2303.06365","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-self-supervised-time-series","title":"Multi-Task Self-Supervised Time-Series Representation Learning","date":"2023-03-02","arxiv_id":"2303.01034","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-neuronal-and-synaptic-dynamics","title":"Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles","date":"2023-02-22","arxiv_id":"2302.11618","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedst-federated-shapelet-transformation-for","title":"FedST: Secure Federated Shapelet Transformation for Time Series Classification","date":"2023-02-21","arxiv_id":"2302.10631","repositories_listed":0,"syntology":null},{"url":null,"slug":"excess-risk-bound-for-deep-learning-under","title":"Excess risk bound for deep learning under weak dependence","date":"2023-02-15","arxiv_id":"2302.07503","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-ps-weakly-dependent","title":"Deep learning for $ψ$-weakly dependent processes","date":"2023-02-01","arxiv_id":"2302.00333","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterizing-the-cost-function-of-dynamic","title":"Parameterizing the cost function of Dynamic Time Warping with application to time series classification","date":"2023-01-24","arxiv_id":"2301.10350","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-time-series-approach-to-parkinson-s-disease","title":"Interpretable Classification of Early Stage Parkinson's Disease from EEG","date":"2023-01-20","arxiv_id":"2301.09568","repositories_listed":0,"syntology":null},{"url":null,"slug":"lb-simtsc-an-efficient-similarity-aware-graph","title":"LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification","date":"2023-01-12","arxiv_id":"2301.04838","repositories_listed":0,"syntology":null},{"url":null,"slug":"popnasv3-a-pareto-optimal-neural-architecture","title":"POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification","date":"2022-12-13","arxiv_id":"2212.06735","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dope-distance-is-sic-a-stable-informative","title":"The DOPE Distance is SIC: A Stable, Informative, and Computable Metric on Time Series And Ordered Merge Trees","date":"2022-12-03","arxiv_id":"2212.01648","repositories_listed":0,"syntology":null},{"url":null,"slug":"gated-recurrent-neural-networks-with-weighted","title":"Gated Recurrent Neural Networks with Weighted Time-Delay Feedback","date":"2022-12-01","arxiv_id":"2212.00228","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-specific-attention-csa-for-time-series","title":"Class-Specific Attention (CSA) for Time-Series Classification","date":"2022-11-19","arxiv_id":"2211.10609","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterization-of-state-duration-in-hidden","title":"Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography","date":"2022-11-17","arxiv_id":"2211.09478","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-label-time-series-classification","title":"A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring","date":"2022-09-30","arxiv_id":"2210.00089","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-symbolic-models-for-interpretable-time","title":"Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description","date":"2022-09-15","arxiv_id":"2209.09114","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evaluation-of-low-overhead-time-series","title":"An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning","date":"2022-09-12","arxiv_id":"2209.05300","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-situ-animal-behavior-classification-using","title":"In-situ animal behavior classification using knowledge distillation and fixed-point quantization","date":"2022-09-09","arxiv_id":"2209.04130","repositories_listed":0,"syntology":null},{"url":null,"slug":"lets-gzsl-a-latent-embedding-model-for-time","title":"LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning","date":"2022-07-25","arxiv_id":"2207.12007","repositories_listed":0,"syntology":null}],"record_sha256":"a851b41efa2ef3836cbf030c09611fb718d4ed4208a017189fd8c1e01d981b7c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}