{"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/16","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":16,"pages_in_order":68,"rows_per_page":100,"rows":[1501,1600],"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/15","next":"/task/time-series/papers/17","papers":[{"url":"/paper/modelling-time-varying-interactions-in","slug":"modelling-time-varying-interactions-in","title":"Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model","date":"2020-07-30","arxiv_id":"2007.15545","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-recurrent-neural-networks-and","slug":"rethinking-recurrent-neural-networks-and","title":"Rethinking Recurrent Neural Networks and Other Improvements for Image Classification","date":"2020-07-30","arxiv_id":"2007.15161","repositories_listed":1,"syntology":null},{"url":"/paper/multi-output-gaussian-processes-with","slug":"multi-output-gaussian-processes-with","title":"Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment","date":"2020-07-28","arxiv_id":"2007.14052","repositories_listed":1,"syntology":null},{"url":"/paper/calibration-of-google-trends-time-series","slug":"calibration-of-google-trends-time-series","title":"Calibration of Google Trends Time Series","date":"2020-07-27","arxiv_id":"2007.13861","repositories_listed":1,"syntology":null},{"url":"/paper/graph-gamma-process-generalized-linear","slug":"graph-gamma-process-generalized-linear","title":"Graph Gamma Process Generalized Linear Dynamical Systems","date":"2020-07-25","arxiv_id":"2007.12852","repositories_listed":1,"syntology":null},{"url":"/paper/espresso-entropy-and-shape-aware-time-series","slug":"espresso-entropy-and-shape-aware-time-series","title":"ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data","date":"2020-07-24","arxiv_id":"2008.03230","repositories_listed":1,"syntology":null},{"url":"/paper/deep-dynamic-factor-models","slug":"deep-dynamic-factor-models","title":"Deep Dynamic Factor Models","date":"2020-07-23","arxiv_id":"2007.11887","repositories_listed":1,"syntology":null},{"url":"/paper/hide-and-seek-privacy-challenge","slug":"hide-and-seek-privacy-challenge","title":"Hide-and-Seek Privacy Challenge","date":"2020-07-23","arxiv_id":"2007.12087","repositories_listed":1,"syntology":null},{"url":"/paper/fused-lasso-regularized-cholesky-factors-of","slug":"fused-lasso-regularized-cholesky-factors-of","title":"Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data","date":"2020-07-22","arxiv_id":"2007.11168","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-brazilian-and-american-covid-19","slug":"forecasting-brazilian-and-american-covid-19","title":"Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables","date":"2020-07-21","arxiv_id":"2007.10981","repositories_listed":1,"syntology":null},{"url":"/paper/magma-inference-and-prediction-with-multi","slug":"magma-inference-and-prediction-with-multi","title":"MAGMA: Inference and Prediction with Multi-Task Gaussian Processes","date":"2020-07-21","arxiv_id":"2007.10731","repositories_listed":1,"syntology":null},{"url":"/paper/short-term-forecasting-covid-19-cumulative","slug":"short-term-forecasting-covid-19-cumulative","title":"Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil","date":"2020-07-21","arxiv_id":"2007.12261","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-evaluation-of-multi-task","slug":"a-comprehensive-evaluation-of-multi-task","title":"A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data","date":"2020-07-20","arxiv_id":"2007.10185","repositories_listed":1,"syntology":null},{"url":"/paper/covid-19-data-analysis-and-forecasting","slug":"covid-19-data-analysis-and-forecasting","title":"COVID-19 Data Analysis and Forecasting: Algeria and the World","date":"2020-07-19","arxiv_id":"2007.09755","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-the-self-transition-probability-of","slug":"leveraging-the-self-transition-probability-of","title":"Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode Classification","date":"2020-07-16","arxiv_id":"2007.08687","repositories_listed":1,"syntology":null},{"url":"/paper/timexplain-a-framework-for-explaining-the","slug":"timexplain-a-framework-for-explaining-the","title":"timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers","date":"2020-07-15","arxiv_id":"2007.07606","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/timexplain-a-framework-for-explaining-the#ran","syntology_url":"https://syntology.ai/paper/2007.07606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07606"}},"official":{"repos":["loadingbyte/timexplain"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-financial-time-series-using-lstm","slug":"modeling-financial-time-series-using-lstm","title":"Modeling Financial Time Series using LSTM with Trainable Initial Hidden States","date":"2020-07-14","arxiv_id":"2007.06848","repositories_listed":1,"syntology":null},{"url":"/paper/learning-latent-stochastic-differential","slug":"learning-latent-stochastic-differential","title":"Identifying Latent Stochastic Differential Equations","date":"2020-07-12","arxiv_id":"2007.06075","repositories_listed":1,"syntology":null},{"url":"/paper/fast-variational-learning-in-state-space","slug":"fast-variational-learning-in-state-space","title":"Fast Variational Learning in State-Space Gaussian Process Models","date":"2020-07-09","arxiv_id":"2007.04731","repositories_listed":1,"syntology":null},{"url":"/paper/learning-differential-equations-that-are-easy","slug":"learning-differential-equations-that-are-easy","title":"Learning Differential Equations that are Easy to Solve","date":"2020-07-09","arxiv_id":"2007.04504","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-differential-equations-that-are-easy#ran","syntology_url":"https://syntology.ai/paper/2007.04504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04504"}},"official":{"repos":["jacobjinkelly/easy-neural-ode"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/long-short-term-memory-spiking-networks-and","slug":"long-short-term-memory-spiking-networks-and","title":"Long Short-Term Memory Spiking Networks and Their Applications","date":"2020-07-09","arxiv_id":"2007.04779","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-one-class-classification-via-meta-1","slug":"few-shot-one-class-classification-via-meta-1","title":"Few-Shot One-Class Classification via Meta-Learning","date":"2020-07-08","arxiv_id":"2007.04146","repositories_listed":1,"syntology":null},{"url":"/paper/mr-estimator-a-toolbox-to-determine-intrinsic","slug":"mr-estimator-a-toolbox-to-determine-intrinsic","title":"MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity","date":"2020-07-07","arxiv_id":"2007.03367","repositories_listed":1,"syntology":null},{"url":"/paper/non-image-data-classification-with","slug":"non-image-data-classification-with","title":"Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis","date":"2020-07-07","arxiv_id":"2007.03218","repositories_listed":1,"syntology":null},{"url":"/paper/superiority-of-simplicity-a-lightweight-model","slug":"superiority-of-simplicity-a-lightweight-model","title":"Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction","date":"2020-07-07","arxiv_id":"2007.03568","repositories_listed":1,"syntology":null},{"url":"/paper/examining-covid-19-forecasting-using-spatio","slug":"examining-covid-19-forecasting-using-spatio","title":"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks","date":"2020-07-06","arxiv_id":"2007.03113","repositories_listed":1,"syntology":null},{"url":"/paper/metric-guided-prototype-learning","slug":"metric-guided-prototype-learning","title":"Leveraging Class Hierarchies with Metric-Guided Prototype Learning","date":"2020-07-06","arxiv_id":"2007.03047","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-forecasting-of-bitcoin-prices","slug":"time-series-forecasting-of-bitcoin-prices","title":"Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach","date":"2020-07-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/high-recall-causal-discovery-for","slug":"high-recall-causal-discovery-for","title":"High-recall causal discovery for autocorrelated time series with latent confounders","date":"2020-07-03","arxiv_id":"2007.01884","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-characterization-of-non-uniformly","slug":"accurate-characterization-of-non-uniformly","title":"Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential Equations","date":"2020-07-02","arxiv_id":"2007.01073","repositories_listed":1,"syntology":null},{"url":"/paper/path-signatures-on-lie-groups","slug":"path-signatures-on-lie-groups","title":"Path Signatures on Lie Groups","date":"2020-07-02","arxiv_id":"2007.06633","repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-temporal-self-attention-for","slug":"lightweight-temporal-self-attention-for","title":"Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series","date":"2020-07-01","arxiv_id":"2007.00586","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-gan-for-timeseries-generation","slug":"conditional-gan-for-timeseries-generation","title":"Conditional GAN for timeseries generation","date":"2020-06-30","arxiv_id":"2006.16477","repositories_listed":1,"syntology":null},{"url":"/paper/subject-aware-contrastive-learning-for","slug":"subject-aware-contrastive-learning-for","title":"Subject-Aware Contrastive Learning for Biosignals","date":"2020-06-30","arxiv_id":"2007.04871","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-precipitable-water-vapor-using","slug":"forecasting-precipitable-water-vapor-using","title":"Forecasting Precipitable Water Vapor Using LSTMs","date":"2020-06-26","arxiv_id":"2006.15165","repositories_listed":1,"syntology":null},{"url":"/paper/a-model-of-the-fed-s-view-on-inflation","slug":"a-model-of-the-fed-s-view-on-inflation","title":"A Model of the Fed's View on Inflation","date":"2020-06-25","arxiv_id":"2006.14110","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-of-temporal-non-negative","slug":"a-comparative-study-of-temporal-non-negative","title":"A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization","date":"2020-06-23","arxiv_id":"2006.12843","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-regression","slug":"time-series-regression","title":"Time Series Extrinsic Regression","date":"2020-06-23","arxiv_id":"2006.12672","repositories_listed":1,"syntology":null},{"url":"/paper/aligning-time-series-on-incomparable-spaces","slug":"aligning-time-series-on-incomparable-spaces","title":"Aligning Time Series on Incomparable Spaces","date":"2020-06-22","arxiv_id":"2006.12648","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/aligning-time-series-on-incomparable-spaces#ran","syntology_url":"https://syntology.ai/paper/2006.12648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12648"}},"official":{"repos":["samcohen16/Aligning-Time-Series"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hidden-markov-nonlinear-ica-unsupervised","slug":"hidden-markov-nonlinear-ica-unsupervised","title":"Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series","date":"2020-06-22","arxiv_id":"2006.12107","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/hidden-markov-nonlinear-ica-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2006.12107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12107"}},"official":{"repos":["HHalva/hmnlica"],"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/frequentist-uncertainty-in-recurrent-neural","slug":"frequentist-uncertainty-in-recurrent-neural","title":"Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions","date":"2020-06-20","arxiv_id":"2006.13707","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-implementations-of-echo-state","slug":"efficient-implementations-of-echo-state","title":"Efficient implementations of echo state network cross-validation","date":"2020-06-19","arxiv_id":"2006.11282","repositories_listed":1,"syntology":null},{"url":"/paper/supporting-optimal-phase-space","slug":"supporting-optimal-phase-space","title":"Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling","date":"2020-06-19","arxiv_id":"2006.11381","repositories_listed":1,"syntology":null},{"url":"/paper/amortized-causal-discovery-learning-to-infer","slug":"amortized-causal-discovery-learning-to-infer","title":"Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data","date":"2020-06-18","arxiv_id":"2006.10833","repositories_listed":1,"syntology":null},{"url":"/paper/low-rank-autoregressive-tensor-completion-for","slug":"low-rank-autoregressive-tensor-completion-for","title":"Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting","date":"2020-06-18","arxiv_id":"2006.10436","repositories_listed":1,"syntology":null},{"url":"/paper/online-change-point-detection-in-molecular","slug":"online-change-point-detection-in-molecular","title":"Online Change Point Detection in Molecular Dynamics With Optical Random Features","date":"2020-06-15","arxiv_id":"2006.08697","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-phenotyping-using-deep-predictive","slug":"temporal-phenotyping-using-deep-predictive","title":"Temporal Phenotyping using Deep Predictive Clustering of Disease Progression","date":"2020-06-15","arxiv_id":"2006.08600","repositories_listed":1,"syntology":null},{"url":"/paper/inductive-graph-neural-networks-for","slug":"inductive-graph-neural-networks-for","title":"Inductive Graph Neural Networks for Spatiotemporal Kriging","date":"2020-06-13","arxiv_id":"2006.07527","repositories_listed":1,"syntology":null},{"url":"/paper/reservoir-computing-meets-recurrent-kernels","slug":"reservoir-computing-meets-recurrent-kernels","title":"Reservoir Computing meets Recurrent Kernels and Structured Transforms","date":"2020-06-12","arxiv_id":"2006.07310","repositories_listed":1,"syntology":null},{"url":"/paper/seq2tens-an-efficient-representation-of","slug":"seq2tens-an-efficient-representation-of","title":"Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections","date":"2020-06-12","arxiv_id":"2006.07027","repositories_listed":1,"syntology":null},{"url":"/paper/automating-cluster-analysis-to-generate","slug":"automating-cluster-analysis-to-generate","title":"Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa","date":"2020-06-11","arxiv_id":"2006.07197","repositories_listed":1,"syntology":null},{"url":"/paper/crisp-a-probabilistic-model-for-individual","slug":"crisp-a-probabilistic-model-for-individual","title":"CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data","date":"2020-06-09","arxiv_id":"2006.04942","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-structural-perturbations-from-time","slug":"detecting-structural-perturbations-from-time","title":"Detecting structural perturbations from time series with deep learning","date":"2020-06-09","arxiv_id":"2006.05232","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-dynamic-distribution-decomposition","slug":"sparse-dynamic-distribution-decomposition","title":"Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data","date":"2020-06-09","arxiv_id":"2006.05138","repositories_listed":1,"syntology":null},{"url":"/paper/wavelet-networks-scale-equivariant-learning","slug":"wavelet-networks-scale-equivariant-learning","title":"Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series","date":"2020-06-09","arxiv_id":"2006.05259","repositories_listed":1,"syntology":null},{"url":"/paper/deep-stock-predictions","slug":"deep-stock-predictions","title":"Deep Stock Predictions","date":"2020-06-08","arxiv_id":"2006.04992","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-time-warping-as-a-new-evaluation-for","slug":"dynamic-time-warping-as-a-new-evaluation-for","title":"Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning","date":"2020-06-08","arxiv_id":"2006.04667","repositories_listed":1,"syntology":null},{"url":"/paper/enk-encoding-time-information-in-convolution","slug":"enk-encoding-time-information-in-convolution","title":"EnK: Encoding time-information in convolution","date":"2020-06-07","arxiv_id":"2006.04198","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-deep-learning-framework-for","slug":"attention-based-deep-learning-framework-for","title":"Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation","date":"2020-06-06","arxiv_id":"2006.03820","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-time-series-classification-on","slug":"interpretable-time-series-classification-on","title":"Interpretable Time-series Classification on Few-shot Samples","date":"2020-06-03","arxiv_id":"2006.02031","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-gravitational-wave-signals-from","slug":"detection-of-gravitational-wave-signals-from","title":"Detection of gravitational-wave signals from binary neutron star mergers using machine learning","date":"2020-06-02","arxiv_id":"2006.01509","repositories_listed":1,"syntology":null},{"url":"/paper/a-generalised-signature-method-for-time","slug":"a-generalised-signature-method-for-time","title":"A Generalised Signature Method for Multivariate Time Series Feature Extraction","date":"2020-06-01","arxiv_id":"2006.00873","repositories_listed":1,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/a-generalised-signature-method-for-time#ran","syntology_url":"https://syntology.ai/paper/2006.00873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.00873"}},"official":{"repos":["jambo6/generalised-signature-method"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-synchronized-subsets-of-sequences","slug":"discovering-synchronized-subsets-of-sequences","title":"Discovering Synchronized Subsets of Sequences: A Large Scale Solution","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-time-series-classification-1","slug":"interpretable-time-series-classification-1","title":"Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations","date":"2020-05-31","arxiv_id":"2006.01667","repositories_listed":1,"syntology":null},{"url":"/paper/theory-and-algorithms-for-shapelet-based","slug":"theory-and-algorithms-for-shapelet-based","title":"Theory and Algorithms for Shapelet-based Multiple-Instance Learning","date":"2020-05-31","arxiv_id":"2006.01130","repositories_listed":1,"syntology":null},{"url":"/paper/learning-efficient-representations-of-mouse","slug":"learning-efficient-representations-of-mouse","title":"Learning Efficient Representations of Mouse Movements to Predict User Attention","date":"2020-05-30","arxiv_id":"2006.01644","repositories_listed":1,"syntology":null},{"url":"/paper/online-regulation-of-unstable-lti-systems","slug":"online-regulation-of-unstable-lti-systems","title":"On Regularizability and its Application to Online Control of Unstable LTI Systems","date":"2020-05-29","arxiv_id":"2006.00125","repositories_listed":1,"syntology":null},{"url":"/paper/discretize-optimize-vs-optimize-discretize","slug":"discretize-optimize-vs-optimize-discretize","title":"Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows","date":"2020-05-27","arxiv_id":"2005.13420","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":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) · 1 unverified","sample_list":"/paper/discretize-optimize-vs-optimize-discretize#ran","syntology_url":"https://syntology.ai/paper/2005.13420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.13420"}},"official":{"repos":["EmoryMLIP/DOvsOD_NeuralODEs"],"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/machine-learning-based-unbalance-detection-of","slug":"machine-learning-based-unbalance-detection-of","title":"Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data","date":"2020-05-26","arxiv_id":"2005.12742","repositories_listed":1,"syntology":null},{"url":"/paper/a-bayesian-inspired-deep-learning-semi","slug":"a-bayesian-inspired-deep-learning-semi","title":"A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping","date":"2020-05-25","arxiv_id":"2005.11930","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-super-resolution-for-downscaling","slug":"stochastic-super-resolution-for-downscaling","title":"Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network","date":"2020-05-20","arxiv_id":"2005.10374","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/stochastic-super-resolution-for-downscaling#ran","syntology_url":"https://syntology.ai/paper/2005.10374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10374"}},"official":{"repos":["jleinonen/downscaling-rnn-gan"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/forecasting-with-sktime-designing-sktime-s","slug":"forecasting-with-sktime-designing-sktime-s","title":"Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study","date":"2020-05-16","arxiv_id":"2005.08067","repositories_listed":1,"syntology":null},{"url":"/paper/multi-step-ahead-prediction-from-short-term","slug":"multi-step-ahead-prediction-from-short-term","title":"DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation","date":"2020-05-16","arxiv_id":"2005.07842","repositories_listed":1,"syntology":null},{"url":"/paper/red-deep-recurrent-neural-networks-for-sleep","slug":"red-deep-recurrent-neural-networks-for-sleep","title":"RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection","date":"2020-05-15","arxiv_id":"2005.07795","repositories_listed":1,"syntology":null},{"url":"/paper/atspy-automated-time-series-forecasting-in","slug":"atspy-automated-time-series-forecasting-in","title":"AtsPy: Automated Time Series Forecasting in Python","date":"2020-05-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-arrhythmia-by-using-deep","slug":"classification-of-arrhythmia-by-using-deep","title":"Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation","date":"2020-05-14","arxiv_id":"2005.06902","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-enhanced-neural-fashion-trend","slug":"knowledge-enhanced-neural-fashion-trend","title":"Knowledge Enhanced Neural Fashion Trend Forecasting","date":"2020-05-07","arxiv_id":"2005.03297","repositories_listed":1,"syntology":null},{"url":"/paper/local-cascade-ensemble-for-multivariate-data","slug":"local-cascade-ensemble-for-multivariate-data","title":"XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification","date":"2020-05-07","arxiv_id":"2005.03645","repositories_listed":1,"syntology":null},{"url":"/paper/on-a-computationally-scalable-sparse","slug":"on-a-computationally-scalable-sparse","title":"On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principle","date":"2020-05-07","arxiv_id":"2005.03253","repositories_listed":1,"syntology":null},{"url":"/paper/deep-recurrent-disease-progression-model-for","slug":"deep-recurrent-disease-progression-model-for","title":"Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression","date":"2020-05-06","arxiv_id":"2005.02643","repositories_listed":1,"syntology":null},{"url":"/paper/if-you-like-it-gan-it-probabilistic","slug":"if-you-like-it-gan-it-probabilistic","title":"If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN","date":"2020-05-03","arxiv_id":"2005.01181","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/if-you-like-it-gan-it-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2005.01181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01181"}},"official":null}},{"url":"/paper/on-time-series-representations-for-multi","slug":"on-time-series-representations-for-multi","title":"On time series representations for multi-label NILM","date":"2020-05-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sleepposenet-multi-view-learning-for-sleep","slug":"sleepposenet-multi-view-learning-for-sleep","title":"SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB","date":"2020-05-02","arxiv_id":"2005.02176","repositories_listed":1,"syntology":null},{"url":"/paper/does-terrorism-trigger-online-hate-speech-on","slug":"does-terrorism-trigger-online-hate-speech-on","title":"Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series","date":"2020-04-30","arxiv_id":"2004.14733","repositories_listed":1,"syntology":null},{"url":"/paper/forecasting-in-non-stationary-environments","slug":"forecasting-in-non-stationary-environments","title":"Forecasting in Non-stationary Environments with Fuzzy Time Series","date":"2020-04-27","arxiv_id":"2004.12554","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-unsupervised-change-point","slug":"explainable-unsupervised-change-point","title":"Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks","date":"2020-04-24","arxiv_id":"2004.11934","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-find-a-unicorn-a-novel-model-free","slug":"how-to-find-a-unicorn-a-novel-model-free","title":"How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series","date":"2020-04-23","arxiv_id":"2004.11468","repositories_listed":1,"syntology":null},{"url":"/paper/matchboxnet-1d-time-channel-separable-1","slug":"matchboxnet-1d-time-channel-separable-1","title":"MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition","date":"2020-04-21","arxiv_id":"2004.08531","repositories_listed":1,"syntology":null},{"url":"/paper/oversampling-for-imbalanced-time-series-data","slug":"oversampling-for-imbalanced-time-series-data","title":"Minority Oversampling for Imbalanced Time Series Classification","date":"2020-04-14","arxiv_id":"2004.06373","repositories_listed":1,"syntology":null},{"url":"/paper/k-nearest-neighbour-classifiers-2nd-edition","slug":"k-nearest-neighbour-classifiers-2nd-edition","title":"k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)","date":"2020-04-09","arxiv_id":"2004.04523","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-spatial-transformers-for","slug":"probabilistic-spatial-transformers-for","title":"Probabilistic Spatial Transformer Networks","date":"2020-04-07","arxiv_id":"2004.03637","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-sticky-hierarchical-dirichlet","slug":"disentangled-sticky-hierarchical-dirichlet","title":"Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model","date":"2020-04-06","arxiv_id":"2004.03019","repositories_listed":1,"syntology":null},{"url":"/paper/a-spatio-temporal-spot-forecasting-framework","slug":"a-spatio-temporal-spot-forecasting-framework","title":"A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction","date":"2020-03-31","arxiv_id":"2003.13977","repositories_listed":1,"syntology":null},{"url":"/paper/coronavirus-optimization-algorithm-a","slug":"coronavirus-optimization-algorithm-a","title":"Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model","date":"2020-03-30","arxiv_id":"2003.13633","repositories_listed":1,"syntology":null},{"url":"/paper/pruned-wasserstein-index-generation-model-and","slug":"pruned-wasserstein-index-generation-model-and","title":"Pruned Wasserstein Index Generation Model and wigpy Package","date":"2020-03-30","arxiv_id":"2004.00999","repositories_listed":1,"syntology":null},{"url":"/paper/proximity-based-active-learning-on-streaming","slug":"proximity-based-active-learning-on-streaming","title":"Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition","date":"2020-03-29","arxiv_id":"2003.13098","repositories_listed":1,"syntology":null},{"url":"/paper/correlated-daily-time-series-and-forecasting","slug":"correlated-daily-time-series-and-forecasting","title":"Correlated daily time series and forecasting in the M4 competition","date":"2020-03-28","arxiv_id":"2003.12796","repositories_listed":1,"syntology":null},{"url":"/paper/fastdtw-is-approximate-and-generally-slower","slug":"fastdtw-is-approximate-and-generally-slower","title":"FastDTW is approximate and Generally Slower than the Algorithm it Approximates","date":"2020-03-25","arxiv_id":"2003.11246","repositories_listed":1,"syntology":null},{"url":"/paper/r-force-robust-learning-for-random-recurrent","slug":"r-force-robust-learning-for-random-recurrent","title":"R-FORCE: Robust Learning for Random Recurrent Neural Networks","date":"2020-03-25","arxiv_id":"2003.11660","repositories_listed":1,"syntology":null},{"url":"/paper/deep-markov-spatio-temporal-factorization","slug":"deep-markov-spatio-temporal-factorization","title":"Deep Markov Spatio-Temporal Factorization","date":"2020-03-22","arxiv_id":"2003.09779","repositories_listed":1,"syntology":null}],"record_sha256":"1ae11802542691dff7f1c8657e432865e4a3e732276327cfdcba0ca2d6a28210","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}