{"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/papers/8","list_of":"/task/time-series","task":"Time Series Analysis","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":8,"pages_in_order":68,"rows_per_page":100,"rows":[701,800],"of":6748,"counts":{"archive_papers_tagged":6748,"with_a_code_link":1993,"where_syntology_ran_a_sample":383,"not_listed_spam_title":0,"listed":6748,"listed_where_code_ran":383,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":327,"every_run_a_failure_of_syntologys_instrument":56,"listed_with_a_run_with_no_instrument_failure":327,"listed_every_run_a_failure_of_syntologys_instrument":56,"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","prev":"/task/time-series/papers/7","next":"/task/time-series/papers/9","papers":[{"url":"/paper/tilde-q-a-transformation-invariant-loss","slug":"tilde-q-a-transformation-invariant-loss","title":"TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting","date":"2022-10-26","arxiv_id":"2210.15050","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tilde-q-a-transformation-invariant-loss#ran","syntology_url":"https://syntology.ai/paper/2210.15050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15050"}},"official":{"repos":["hyunwookl/tilde-q"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fingerflex-inferring-finger-trajectories-from","slug":"fingerflex-inferring-finger-trajectories-from","title":"FingerFlex: Inferring Finger Trajectories from ECoG signals","date":"2022-10-23","arxiv_id":"2211.01960","repositories_listed":1,"syntology":null},{"url":"/paper/spectranet-multivariate-forecasting-and","slug":"spectranet-multivariate-forecasting-and","title":"SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data","date":"2022-10-22","arxiv_id":"2210.12515","repositories_listed":1,"syntology":null},{"url":"/paper/neural-odes-as-feedback-policies-for","slug":"neural-odes-as-feedback-policies-for","title":"Neural ODEs as Feedback Policies for Nonlinear Optimal Control","date":"2022-10-20","arxiv_id":"2210.11245","repositories_listed":1,"syntology":null},{"url":"/paper/anytime-valid-off-policy-inference-for","slug":"anytime-valid-off-policy-inference-for","title":"Anytime-valid off-policy inference for contextual bandits","date":"2022-10-19","arxiv_id":"2210.10768","repositories_listed":1,"syntology":null},{"url":"/paper/improving-medical-predictions-by-irregular","slug":"improving-medical-predictions-by-irregular","title":"Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling","date":"2022-10-18","arxiv_id":"2210.12156","repositories_listed":1,"syntology":null},{"url":"/paper/soil-moisture-estimation-from-sentinel-1","slug":"soil-moisture-estimation-from-sentinel-1","title":"Soil moisture estimation from Sentinel-1 interferometric observations over arid regions","date":"2022-10-18","arxiv_id":"2210.10665","repositories_listed":1,"syntology":null},{"url":"/paper/tfad-a-decomposition-time-series-anomaly","slug":"tfad-a-decomposition-time-series-anomaly","title":"TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis","date":"2022-10-18","arxiv_id":"2210.09693","repositories_listed":1,"syntology":null},{"url":"/paper/flipped-classroom-effective-teaching-for-time","slug":"flipped-classroom-effective-teaching-for-time","title":"Flipped Classroom: Effective Teaching for Time Series Forecasting","date":"2022-10-17","arxiv_id":"2210.08959","repositories_listed":1,"syntology":null},{"url":"/paper/tegdet-an-extensible-python-library-for","slug":"tegdet-an-extensible-python-library-for","title":"tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs","date":"2022-10-17","arxiv_id":"2210.08847","repositories_listed":1,"syntology":null},{"url":"/paper/topological-data-analysis-for-functional","slug":"topological-data-analysis-for-functional","title":"Dynamic Topological Data Analysis of Functional Human Brain Networks","date":"2022-10-17","arxiv_id":"2210.09092","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-evaluation-of-multivariate-time","slug":"an-empirical-evaluation-of-multivariate-time","title":"An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions","date":"2022-10-14","arxiv_id":"2210.07713","repositories_listed":1,"syntology":null},{"url":"/paper/a-large-scale-annotated-multivariate-time","slug":"a-large-scale-annotated-multivariate-time","title":"A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID","date":"2022-10-13","arxiv_id":"2210.07317","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-in-dynamic-networks","slug":"anomaly-detection-in-dynamic-networks","title":"Anomaly detection in dynamic networks","date":"2022-10-13","arxiv_id":"2210.07407","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-on-the-fly-and-active","slug":"data-augmentation-on-the-fly-and-active","title":"Data augmentation on-the-fly and active learning in data stream classification","date":"2022-10-13","arxiv_id":"2210.06873","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-evaluation-of-data-augmentations","slug":"empirical-evaluation-of-data-augmentations","title":"Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning","date":"2022-10-13","arxiv_id":"2210.06701","repositories_listed":1,"syntology":null},{"url":"/paper/regularized-graph-structure-learning-with","slug":"regularized-graph-structure-learning-with","title":"Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting","date":"2022-10-12","arxiv_id":"2210.06126","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/regularized-graph-structure-learning-with#ran","syntology_url":"https://syntology.ai/paper/2210.06126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06126"}},"official":{"repos":["alipay/rgsl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/class-specific-explainability-for-deep-time","slug":"class-specific-explainability-for-deep-time","title":"Class-Specific Explainability for Deep Time Series Classifiers","date":"2022-10-11","arxiv_id":"2210.05411","repositories_listed":1,"syntology":null},{"url":"/paper/combining-datasets-to-increase-the-number-of","slug":"combining-datasets-to-increase-the-number-of","title":"Combining datasets to increase the number of samples and improve model fitting","date":"2022-10-11","arxiv_id":"2210.05165","repositories_listed":1,"syntology":null},{"url":"/paper/deep-counterfactual-estimation-with","slug":"deep-counterfactual-estimation-with","title":"Deep Counterfactual Estimation with Categorical Background Variables","date":"2022-10-11","arxiv_id":"2210.05811","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-counterfactual-estimation-with#ran","syntology_url":"https://syntology.ai/paper/2210.05811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05811"}},"official":{"repos":["edebrouwer/cfqp"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-explaining-hierarchical-model-for","slug":"self-explaining-hierarchical-model-for","title":"Self-explaining Hierarchical Model for Intraoperative Time Series","date":"2022-10-10","arxiv_id":"2210.04417","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-dynamical-systems","slug":"multi-task-dynamical-systems","title":"Multi-Task Dynamical Systems","date":"2022-10-08","arxiv_id":"2210.04023","repositories_listed":1,"syntology":null},{"url":"/paper/koopman-neural-forecaster-for-time-series","slug":"koopman-neural-forecaster-for-time-series","title":"Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts","date":"2022-10-07","arxiv_id":"2210.03675","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-diagnosis-and-prognosis-by","slug":"continuous-diagnosis-and-prognosis-by","title":"Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks","date":"2022-10-06","arxiv_id":"2210.02719","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-county-level-covid-19-infection","slug":"interpreting-county-level-covid-19-infection","title":"Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models","date":"2022-10-06","arxiv_id":"2210.03258","repositories_listed":1,"syntology":null},{"url":"/paper/degan-time-series-anomaly-detection-using","slug":"degan-time-series-anomaly-detection-using","title":"DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation","date":"2022-10-05","arxiv_id":"2210.02449","repositories_listed":1,"syntology":null},{"url":"/paper/learning-video-independent-eye-contact","slug":"learning-video-independent-eye-contact","title":"Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos","date":"2022-10-05","arxiv_id":"2210.02033","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-based-conditional-generative","slug":"transformer-based-conditional-generative","title":"Transformer-based conditional generative adversarial network for multivariate time series generation","date":"2022-10-05","arxiv_id":"2210.02089","repositories_listed":1,"syntology":null},{"url":"/paper/tripletformer-for-probabilistic-interpolation","slug":"tripletformer-for-probabilistic-interpolation","title":"Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series","date":"2022-10-05","arxiv_id":"2210.02091","repositories_listed":1,"syntology":null},{"url":"/paper/learning-signal-temporal-logic-through-neural","slug":"learning-signal-temporal-logic-through-neural","title":"Learning Signal Temporal Logic through Neural Network for Interpretable Classification","date":"2022-10-04","arxiv_id":"2210.01910","repositories_listed":1,"syntology":null},{"url":"/paper/combined-dynamic-virtual-spatiotemporal-graph","slug":"combined-dynamic-virtual-spatiotemporal-graph","title":"Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction","date":"2022-10-03","arxiv_id":"2210.00704","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-model-selection-for-time-series","slug":"unsupervised-model-selection-for-time-series","title":"Unsupervised Model Selection for Time-series Anomaly Detection","date":"2022-10-03","arxiv_id":"2210.01078","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-model-selection-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2210.01078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01078"}},"official":{"repos":["mononitogoswami/tsad-model-selection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-and-robust-video-based-exercise","slug":"fast-and-robust-video-based-exercise","title":"Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers","date":"2022-10-02","arxiv_id":"2210.00507","repositories_listed":1,"syntology":null},{"url":"/paper/multimodality-multi-lead-ecg-arrhythmia","slug":"multimodality-multi-lead-ecg-arrhythmia","title":"Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning","date":"2022-09-30","arxiv_id":"2210.06297","repositories_listed":1,"syntology":null},{"url":"/paper/a-case-study-of-spatiotemporal-forecasting","slug":"a-case-study-of-spatiotemporal-forecasting","title":"A case study of spatiotemporal forecasting techniques for weather forecasting","date":"2022-09-29","arxiv_id":"2209.14782","repositories_listed":1,"syntology":null},{"url":"/paper/experimental-study-of-time-series-forecasting","slug":"experimental-study-of-time-series-forecasting","title":"Experimental study of time series forecasting methods for groundwater level prediction","date":"2022-09-28","arxiv_id":"2209.13927","repositories_listed":1,"syntology":null},{"url":"/paper/neural-parameter-calibration-for-large-scale","slug":"neural-parameter-calibration-for-large-scale","title":"Neural parameter calibration for large-scale multi-agent models","date":"2022-09-27","arxiv_id":"2209.13565","repositories_listed":1,"syntology":null},{"url":"/paper/liquid-structural-state-space-models","slug":"liquid-structural-state-space-models","title":"Liquid Structural State-Space Models","date":"2022-09-26","arxiv_id":"2209.12951","repositories_listed":1,"syntology":null},{"url":"/paper/neural-state-space-modeling-with-latent","slug":"neural-state-space-modeling-with-latent","title":"Neural State-Space Modeling with Latent Causal-Effect Disentanglement","date":"2022-09-26","arxiv_id":"2209.12387","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-on-financial-time-series-by","slug":"anomaly-detection-on-financial-time-series-by","title":"Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks","date":"2022-09-22","arxiv_id":"2209.11686","repositories_listed":1,"syntology":null},{"url":"/paper/olives-dataset-ophthalmic-labels-for","slug":"olives-dataset-ophthalmic-labels-for","title":"OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics","date":"2022-09-22","arxiv_id":"2209.11195","repositories_listed":1,"syntology":null},{"url":"/paper/an-image-processing-approach-to-identify","slug":"an-image-processing-approach-to-identify","title":"An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar Observatory","date":"2022-09-21","arxiv_id":"2209.10631","repositories_listed":1,"syntology":null},{"url":"/paper/deepvarwt-deep-learning-for-a-var-model-with","slug":"deepvarwt-deep-learning-for-a-var-model-with","title":"DeepVARwT: Deep Learning for a VAR Model with Trend","date":"2022-09-21","arxiv_id":"2209.10587","repositories_listed":1,"syntology":null},{"url":"/paper/dataset-impact-events-for-structural-health","slug":"dataset-impact-events-for-structural-health","title":"Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin Plate","date":"2022-09-20","arxiv_id":"2209.10018","repositories_listed":1,"syntology":null},{"url":"/paper/koopman-theoretic-approach-for-identification","slug":"koopman-theoretic-approach-for-identification","title":"Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data","date":"2022-09-18","arxiv_id":"2209.08618","repositories_listed":1,"syntology":null},{"url":"/paper/dynaconf-dynamic-forecasting-of-non","slug":"dynaconf-dynamic-forecasting-of-non","title":"DynaConF: Dynamic Forecasting of Non-Stationary Time Series","date":"2022-09-17","arxiv_id":"2209.08411","repositories_listed":1,"syntology":null},{"url":"/paper/dynamics-informed-deconvolutional-neural","slug":"dynamics-informed-deconvolutional-neural","title":"Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series","date":"2022-09-16","arxiv_id":"2209.07802","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-learning-of-nonlinear-prediction","slug":"efficient-learning-of-nonlinear-prediction","title":"Efficient learning of nonlinear prediction models with time-series privileged information","date":"2022-09-15","arxiv_id":"2209.07067","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-representations-learning-for-time","slug":"generalized-representations-learning-for-time","title":"Out-of-Distribution Representation Learning for Time Series Classification","date":"2022-09-15","arxiv_id":"2209.07027","repositories_listed":1,"syntology":null},{"url":"/paper/improving-accuracy-and-explainability-of","slug":"improving-accuracy-and-explainability-of","title":"Improving Accuracy and Explainability of Online Handwriting Recognition","date":"2022-09-14","arxiv_id":"2209.09102","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-spatiotemporal-graph-neural-networks","slug":"scalable-spatiotemporal-graph-neural-networks","title":"Scalable Spatiotemporal Graph Neural Networks","date":"2022-09-14","arxiv_id":"2209.06520","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/scalable-spatiotemporal-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2209.06520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06520"}},"official":{"repos":["graph-machine-learning-group/sgp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/time-series-prediction-for-food","slug":"time-series-prediction-for-food","title":"Time Series Prediction for Food sustainability","date":"2022-09-14","arxiv_id":"2209.06889","repositories_listed":1,"syntology":null},{"url":"/paper/fast-fitting-of-neural-ordinary-differential","slug":"fast-fitting-of-neural-ordinary-differential","title":"Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data","date":"2022-09-13","arxiv_id":"2209.06184","repositories_listed":1,"syntology":null},{"url":"/paper/amortised-inference-in-structured-generative","slug":"amortised-inference-in-structured-generative","title":"Structured Recognition for Generative Models with Explaining Away","date":"2022-09-12","arxiv_id":"2209.05212","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-forecasting-of-observations-of","slug":"fairness-in-forecasting-of-observations-of","title":"Fairness in Forecasting of Observations of Linear Dynamical Systems","date":"2022-09-12","arxiv_id":"2209.05274","repositories_listed":1,"syntology":null},{"url":"/paper/autoencoder-based-iterative-modeling-and","slug":"autoencoder-based-iterative-modeling-and","title":"Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm","date":"2022-09-09","arxiv_id":"2209.04213","repositories_listed":1,"syntology":null},{"url":"/paper/w-transformers-a-wavelet-based-transformer","slug":"w-transformers-a-wavelet-based-transformer","title":"W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting","date":"2022-09-08","arxiv_id":"2209.03945","repositories_listed":1,"syntology":null},{"url":"/paper/causal-discovery-for-time-series-with-latent","slug":"causal-discovery-for-time-series-with-latent","title":"Causal discovery for time series with latent confounders","date":"2022-09-07","arxiv_id":"2209.03427","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/causal-discovery-for-time-series-with-latent#ran","syntology_url":"https://syntology.ai/paper/2209.03427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03427"}},"official":{"repos":["christianreiser/correlate"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/time-distance-vision-transformers-in-lung","slug":"time-distance-vision-transformers-in-lung","title":"Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography","date":"2022-09-04","arxiv_id":"2209.01676","repositories_listed":1,"syntology":null},{"url":"/paper/msgnn-a-spectral-graph-neural-network-based","slug":"msgnn-a-spectral-graph-neural-network-based","title":"MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian","date":"2022-09-01","arxiv_id":"2209.00546","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/msgnn-a-spectral-graph-neural-network-based#ran","syntology_url":"https://syntology.ai/paper/2209.00546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.00546"}},"official":{"repos":["sherylhyx/msgnn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/light-curve-completion-and-forecasting-using","slug":"light-curve-completion-and-forecasting-using","title":"Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)","date":"2022-08-31","arxiv_id":"2208.14592","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-architecture-for-unsupervised","slug":"denoising-architecture-for-unsupervised","title":"Denoising Architecture for Unsupervised Anomaly Detection in Time-Series","date":"2022-08-30","arxiv_id":"2208.14337","repositories_listed":1,"syntology":null},{"url":"/paper/somoformer-multi-person-pose-forecasting-with","slug":"somoformer-multi-person-pose-forecasting-with","title":"SoMoFormer: Multi-Person Pose Forecasting with Transformers","date":"2022-08-30","arxiv_id":"2208.14023","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-wind-speed-forecasting-using","slug":"spatio-temporal-wind-speed-forecasting-using","title":"Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures","date":"2022-08-29","arxiv_id":"2208.13585","repositories_listed":1,"syntology":null},{"url":"/paper/global-rtk-positioning-in-graphical-state","slug":"global-rtk-positioning-in-graphical-state","title":"Global RTK Positioning in Graphical State Space","date":"2022-08-27","arxiv_id":"2208.12923","repositories_listed":1,"syntology":null},{"url":"/paper/riesz-quincunx-unet-variational-auto-encoder","slug":"riesz-quincunx-unet-variational-auto-encoder","title":"Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising","date":"2022-08-25","arxiv_id":"2208.12810","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-clustering-with-an-em-algorithm","slug":"time-series-clustering-with-an-em-algorithm","title":"Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models","date":"2022-08-25","arxiv_id":"2208.11907","repositories_listed":1,"syntology":null},{"url":"/paper/dcsf-deep-convolutional-set-functions-for","slug":"dcsf-deep-convolutional-set-functions-for","title":"DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series","date":"2022-08-24","arxiv_id":"2208.11374","repositories_listed":1,"syntology":null},{"url":"/paper/towards-an-awareness-of-time-series-anomaly","slug":"towards-an-awareness-of-time-series-anomaly","title":"Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability","date":"2022-08-24","arxiv_id":"2208.11264","repositories_listed":1,"syntology":null},{"url":"/paper/inter-and-intra-series-embeddings-fusion","slug":"inter-and-intra-series-embeddings-fusion","title":"Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting","date":"2022-08-23","arxiv_id":"2208.11515","repositories_listed":1,"syntology":null},{"url":"/paper/shapelet-based-counterfactual-explanations","slug":"shapelet-based-counterfactual-explanations","title":"Shapelet-Based Counterfactual Explanations for Multivariate Time Series","date":"2022-08-22","arxiv_id":"2208.10462","repositories_listed":1,"syntology":null},{"url":"/paper/stop-hop-early-classification-of-irregular","slug":"stop-hop-early-classification-of-irregular","title":"Stop&Hop: Early Classification of Irregular Time Series","date":"2022-08-21","arxiv_id":"2208.09795","repositories_listed":1,"syntology":null},{"url":"/paper/from-time-series-to-networks-in-r-with-the","slug":"from-time-series-to-networks-in-r-with-the","title":"From Time Series to Networks in R with the ts2net Package","date":"2022-08-20","arxiv_id":"2208.09660","repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-short-and-long-term-mask","slug":"an-unsupervised-short-and-long-term-mask","title":"An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection","date":"2022-08-19","arxiv_id":"2208.09240","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-time-series-imputation-and","slug":"diffusion-based-time-series-imputation-and","title":"Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models","date":"2022-08-19","arxiv_id":"2208.09399","repositories_listed":1,"syntology":null},{"url":"/paper/expressing-multivariate-time-series-as-graphs","slug":"expressing-multivariate-time-series-as-graphs","title":"Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer","date":"2022-08-19","arxiv_id":"2208.09300","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-informed-revenue-extrapolation","slug":"simulation-informed-revenue-extrapolation","title":"Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data","date":"2022-08-19","arxiv_id":"2208.10375","repositories_listed":1,"syntology":null},{"url":"/paper/a-two-stream-convolutional-network-for","slug":"a-two-stream-convolutional-network-for","title":"A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction","date":"2022-08-18","arxiv_id":"2208.08848","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-data-driven-gap-filling-of","slug":"efficient-data-driven-gap-filling-of","title":"Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions","date":"2022-08-18","arxiv_id":"2208.08781","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-comparison-of-explainable","slug":"an-empirical-comparison-of-explainable","title":"An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury","date":"2022-08-13","arxiv_id":"2208.06717","repositories_listed":1,"syntology":null},{"url":"/paper/feature-based-time-series-analysis-in-r-using","slug":"feature-based-time-series-analysis-in-r-using","title":"Feature-Based Time-Series Analysis in R using the theft Package","date":"2022-08-12","arxiv_id":"2208.06146","repositories_listed":1,"syntology":null},{"url":"/paper/towards-coupling-full-disk-and-active-region","slug":"towards-coupling-full-disk-and-active-region","title":"Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting","date":"2022-08-11","arxiv_id":"2209.07406","repositories_listed":1,"syntology":null},{"url":"/paper/tsinterpret-a-unified-framework-for-time","slug":"tsinterpret-a-unified-framework-for-time","title":"TSInterpret: A unified framework for time series interpretability","date":"2022-08-10","arxiv_id":"2208.05280","repositories_listed":1,"syntology":null},{"url":"/paper/recovering-the-graph-underlying-networked","slug":"recovering-the-graph-underlying-networked","title":"Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach","date":"2022-08-08","arxiv_id":"2208.04405","repositories_listed":1,"syntology":null},{"url":"/paper/granger-causality-using-neural-networks","slug":"granger-causality-using-neural-networks","title":"Granger Causality using Neural Networks","date":"2022-08-07","arxiv_id":"2208.03703","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-algorithms-for-causal-inference","slug":"forecasting-algorithms-for-causal-inference","title":"Forecasting Algorithms for Causal Inference with Panel Data","date":"2022-08-06","arxiv_id":"2208.03489","repositories_listed":1,"syntology":null},{"url":"/paper/coper-continuous-patient-state-perceiver","slug":"coper-continuous-patient-state-perceiver","title":"COPER: Continuous Patient State Perceiver","date":"2022-08-05","arxiv_id":"2208.03196","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-segmentation-of-the-placenta-in","slug":"automatic-segmentation-of-the-placenta-in","title":"Automatic Segmentation of the Placenta in BOLD MRI Time Series","date":"2022-08-04","arxiv_id":"2208.02895","repositories_listed":1,"syntology":null},{"url":"/paper/visually-evaluating-generative-adversarial","slug":"visually-evaluating-generative-adversarial","title":"Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series","date":"2022-08-04","arxiv_id":"2208.02649","repositories_listed":1,"syntology":null},{"url":"/paper/egpde-net-building-continuous-neural-networks","slug":"egpde-net-building-continuous-neural-networks","title":"EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables","date":"2022-08-03","arxiv_id":"2208.01913","repositories_listed":1,"syntology":null},{"url":"/paper/mfrfnn-multi-functional-recurrent-fuzzy","slug":"mfrfnn-multi-functional-recurrent-fuzzy","title":"MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction","date":"2022-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/predicting-future-mosquito-habitats-using","slug":"predicting-future-mosquito-habitats-using","title":"Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning","date":"2022-08-01","arxiv_id":"2208.01436","repositories_listed":1,"syntology":null},{"url":"/paper/a-tale-of-two-panel-data-regressions","slug":"a-tale-of-two-panel-data-regressions","title":"Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data","date":"2022-07-29","arxiv_id":"2207.14481","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-framework-for-multi-step-ahead","slug":"a-general-framework-for-multi-step-ahead","title":"A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting","date":"2022-07-28","arxiv_id":"2207.14219","repositories_listed":1,"syntology":null},{"url":"/paper/signature-based-models-theory-and-calibration","slug":"signature-based-models-theory-and-calibration","title":"Signature-based models: theory and calibration","date":"2022-07-26","arxiv_id":"2207.13136","repositories_listed":1,"syntology":null},{"url":"/paper/benchmark-time-series-data-sets-for-pytorch","slug":"benchmark-time-series-data-sets-for-pytorch","title":"Benchmark time series data sets for PyTorch -- the torchtime package","date":"2022-07-25","arxiv_id":"2207.12503","repositories_listed":1,"syntology":null},{"url":"/paper/calibrated-one-class-classification-for","slug":"calibrated-one-class-classification-for","title":"Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection","date":"2022-07-25","arxiv_id":"2207.12201","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/calibrated-one-class-classification-for#ran","syntology_url":"https://syntology.ai/paper/2207.12201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12201"}},"official":{"repos":["xuhongzuo/couta"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dcam-dimension-wise-class-activation-map-for","slug":"dcam-dimension-wise-class-activation-map-for","title":"dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification","date":"2022-07-25","arxiv_id":"2207.12165","repositories_listed":1,"syntology":null},{"url":"/paper/domain-invariant-feature-exploration-for","slug":"domain-invariant-feature-exploration-for","title":"Domain-invariant Feature Exploration for Domain Generalization","date":"2022-07-25","arxiv_id":"2207.12020","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/domain-invariant-feature-exploration-for#ran","syntology_url":"https://syntology.ai/paper/2207.12020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12020"}},"official":{"repos":["jindongwang/transferlearning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/codit-conformal-out-of-distribution-detection","slug":"codit-conformal-out-of-distribution-detection","title":"CODiT: Conformal Out-of-Distribution Detection in Time-Series Data","date":"2022-07-24","arxiv_id":"2207.11769","repositories_listed":1,"syntology":null}],"record_sha256":"9d19416bd8c7b0561eca706828c1c4bd1907d768e83fc934088a0fe6957cc74e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}