{"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-1/papers/51","list_of":"/task/time-series-1","task":"Time Series","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":51,"pages_in_order":92,"rows_per_page":100,"rows":[5001,5100],"of":9169,"counts":{"archive_papers_tagged":9169,"with_a_code_link":2973,"where_syntology_ran_a_sample":647,"not_listed_spam_title":0,"listed":9169,"listed_where_code_ran":647,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":559,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":559,"listed_every_run_a_failure_of_syntologys_instrument":88,"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-1","prev":"/task/time-series-1/papers/50","next":"/task/time-series-1/papers/52","papers":[{"url":null,"slug":"the-speckle-contrast-extended-to-the","title":"The speckle contrast extended to the polarimetric case: applications to radar and Laser images","date":"2023-06-08","arxiv_id":"2306.05441","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-domain-soft-sensor","title":"Unsupervised Cross-Domain Soft Sensor Modelling via Deep Physics-Inspired Particle Flow Bayes","date":"2023-06-08","arxiv_id":"2306.04919","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-generative-diffusion-models-for","title":"A Comprehensive Survey on Generative Diffusion Models for Structured Data","date":"2023-06-07","arxiv_id":"2306.04139","repositories_listed":0,"syntology":null},{"url":null,"slug":"nemo-neural-map-growing-system-for","title":"NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark","date":"2023-06-07","arxiv_id":"2306.04540","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-enabled-sleep-staging-from","title":"Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera","date":"2023-06-06","arxiv_id":"2306.03711","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-the-performance-of-us-stock","title":"Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM","date":"2023-06-06","arxiv_id":"2306.03620","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":"nftvis-visual-analysis-of-nft-performance","title":"NFTVis: Visual Analysis of NFT Performance","date":"2023-06-05","arxiv_id":"2306.02712","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-parametric-probabilistic-time-series","title":"Non-parametric Probabilistic Time Series Forecasting via Innovations Representation","date":"2023-06-05","arxiv_id":"2306.03782","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-unrolling-scalable-inverse-free","title":"Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models","date":"2023-06-05","arxiv_id":"2306.03249","repositories_listed":0,"syntology":null},{"url":null,"slug":"pv-fleet-modeling-via-smooth-periodic","title":"PV Fleet Modeling via Smooth Periodic Gaussian Copula","date":"2023-06-05","arxiv_id":"2307.00004","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lktcn-modern-convolution-utilizing","title":"Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting","date":"2023-06-04","arxiv_id":"2306.02326","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-neural-networks-learn-to-classify-chaotic","title":"How neural networks learn to classify chaotic time series","date":"2023-06-04","arxiv_id":"2306.02300","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bio-inspired-chaos-sensor-based-on-the","title":"A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science","date":"2023-06-03","arxiv_id":"2306.01991","repositories_listed":0,"syntology":null},{"url":null,"slug":"gat-gan-a-graph-attention-based-time-series","title":"GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network","date":"2023-06-03","arxiv_id":"2306.01999","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-subgroups-of-icu-patients-using","title":"Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data","date":"2023-06-03","arxiv_id":"2306.02121","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-for-smart-alert-generation","title":"A Hybrid Approach for Smart Alert Generation","date":"2023-06-02","arxiv_id":"2306.07983","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-opc-ua-based-industrial-big-data","title":"An OPC UA-based industrial Big Data architecture","date":"2023-06-02","arxiv_id":"2306.01418","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-fpga-based-implementation-of","title":"Analysis and FPGA based Implementation of Permutation Binary Neural Networks","date":"2023-06-02","arxiv_id":"2306.01383","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-framework-for-uncertainty-1","title":"A General Framework for Uncertainty Quantification via Neural SDE-RNN","date":"2023-06-01","arxiv_id":"2306.01189","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-time-series-model-for","title":"An End-to-End Time Series Model for Simultaneous Imputation and Forecast","date":"2023-06-01","arxiv_id":"2306.00778","repositories_listed":0,"syntology":null},{"url":null,"slug":"otw-optimal-transport-warping-for-time-series","title":"OTW: Optimal Transport Warping for Time Series","date":"2023-06-01","arxiv_id":"2306.00620","repositories_listed":0,"syntology":null},{"url":null,"slug":"crystalgpt-enhancing-system-to-system","title":"CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers","date":"2023-05-31","arxiv_id":"2306.03099","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-estimation-in-panel-data","title":"Deep Neural Network Estimation in Panel Data Models","date":"2023-05-31","arxiv_id":"2305.19921","repositories_listed":0,"syntology":null},{"url":null,"slug":"eamdrift-an-interpretable-self-retrain-model","title":"EAMDrift: An interpretable self retrain model for time series","date":"2023-05-31","arxiv_id":"2305.19837","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-prediction-using-artificial","title":"Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities","date":"2023-05-31","arxiv_id":"2305.19591","repositories_listed":0,"syntology":null},{"url":null,"slug":"impulse-response-analysis-for-structural","title":"Impulse Response Analysis of Structural Nonlinear Time Series Models","date":"2023-05-30","arxiv_id":"2305.19089","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-statistical-feature-guided","slug":"unsupervised-statistical-feature-guided","title":"Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition","date":"2023-05-30","arxiv_id":"2306.05285","repositories_listed":0,"syntology":null},{"url":null,"slug":"networked-time-series-imputation-via-position","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","date":"2023-05-29","arxiv_id":"2305.18612","repositories_listed":0,"syntology":null},{"url":null,"slug":"backdoor-attacks-against-incremental-learners","title":"Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study","date":"2023-05-28","arxiv_id":"2305.18384","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-varying-vector-error-correction-models","title":"Time-Varying Vector Error-Correction Models: Estimation and Inference","date":"2023-05-28","arxiv_id":"2305.17829","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-random-number-generators-and-practical","title":"On random number generators and practical market efficiency","date":"2023-05-27","arxiv_id":"2305.17419","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-method-for-time-series-images","title":"Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology","date":"2023-05-26","arxiv_id":"2305.16656","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-long-short-term-memory-lstm-and","title":"Preliminary studies: Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction","date":"2023-05-26","arxiv_id":"2305.16546","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":"improved-sales-forecasting-using-trend-and","title":"Improved Sales Forecasting using Trend and Seasonality Decomposition with LightGBM","date":"2023-05-26","arxiv_id":"2305.17201","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":"on-the-generalization-capacities-of-neural","title":"On the Generalization and Approximation Capacities of Neural Controlled Differential Equations","date":"2023-05-26","arxiv_id":"2305.16791","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-nev-a-scalable-multi-level-neuroevolution","title":"AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection","date":"2023-05-25","arxiv_id":"2305.16497","repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-consistency-and-partisanship-in-u-s","title":"Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions","date":"2023-05-25","arxiv_id":"2305.16471","repositories_listed":0,"syntology":null},{"url":null,"slug":"rola-a-real-time-online-lightweight-anomaly","title":"RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series","date":"2023-05-25","arxiv_id":"2305.16509","repositories_listed":0,"syntology":null},{"url":null,"slug":"samossa-multivariate-singular-spectrum","title":"SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise","date":"2023-05-25","arxiv_id":"2305.16491","repositories_listed":0,"syntology":null},{"url":null,"slug":"sliding-window-sum-algorithms-for-deep-neural","title":"Sliding Window Sum Algorithms for Deep Neural Networks","date":"2023-05-25","arxiv_id":"2305.16513","repositories_listed":0,"syntology":null},{"url":null,"slug":"stecformer-spatio-temporal-encoding-cascaded","title":"Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting","date":"2023-05-25","arxiv_id":"2305.16370","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-guarantees-of-learning-ensembling","title":"Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting","date":"2023-05-25","arxiv_id":"2305.15786","repositories_listed":0,"syntology":null},{"url":null,"slug":"tlnets-transformation-learning-networks-for","title":"TLNets: Transformation Learning Networks for long-range time-series prediction","date":"2023-05-25","arxiv_id":"2305.15770","repositories_listed":0,"syntology":null},{"url":null,"slug":"validating-a-dynamic-input-output-model-for","title":"Validating a dynamic input-output model for the propagation of supply and demand shocks during the COVID-19 pandemic in Belgium","date":"2023-05-25","arxiv_id":"2305.16377","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-are-few-shot-health","title":"Large Language Models are Few-Shot Health Learners","date":"2023-05-24","arxiv_id":"2305.15525","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-complex-object-changes-in-satellite","title":"Modeling Complex Object Changes in Satellite Image Time-Series: Approach based on CSP and Spatiotemporal Graph","date":"2023-05-24","arxiv_id":"2305.15091","repositories_listed":0,"syntology":null},{"url":null,"slug":"2305-14582","title":"Interpretation of Time-Series Deep Models: A Survey","date":"2023-05-23","arxiv_id":"2305.14582","repositories_listed":0,"syntology":null},{"url":null,"slug":"limited-resource-allocation-in-a-non","title":"Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare","date":"2023-05-22","arxiv_id":"2305.12640","repositories_listed":0,"syntology":null},{"url":null,"slug":"stock-and-market-index-prediction-using","title":"Stock and market index prediction using Informer network","date":"2023-05-22","arxiv_id":"2305.14382","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-mean-squared-error-of-the-ridgeless-least","title":"Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors","date":"2023-05-22","arxiv_id":"2305.12883","repositories_listed":0,"syntology":null},{"url":"/paper/the-federal-reserve-s-response-to-the-global","slug":"the-federal-reserve-s-response-to-the-global","title":"The Federal Reserve's Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis","date":"2023-05-21","arxiv_id":"2305.12318","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-on-intelligent-forecasting-of-credit","title":"Study on Intelligent Forecasting of Credit Bond Default Risk","date":"2023-05-20","arxiv_id":"2305.12142","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-crop-classification-by","title":"Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data","date":"2023-05-19","arxiv_id":"2305.12011","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-change-point-detection-for","title":"Unsupervised Change Point Detection for heterogeneous sensor signals","date":"2023-05-19","arxiv_id":"2305.11976","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-spatiotemporal-enhanced","title":"Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis","date":"2023-05-18","arxiv_id":"2305.10754","repositories_listed":0,"syntology":null},{"url":null,"slug":"spikecp-delay-adaptive-reliable-spiking","title":"Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees","date":"2023-05-18","arxiv_id":"2305.11322","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-estimation-for-covariance","title":"Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models","date":"2023-05-18","arxiv_id":"2305.11282","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-for-stock-trend-prediction-using","title":"Support for Stock Trend Prediction Using Transformers and Sentiment Analysis","date":"2023-05-18","arxiv_id":"2305.14368","repositories_listed":0,"syntology":null},{"url":null,"slug":"imbalanced-aircraft-data-anomaly-detection","title":"Imbalanced Aircraft Data Anomaly Detection","date":"2023-05-17","arxiv_id":"2305.10082","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-consistency-of-signatures-using-lasso","title":"On Consistency of Signature Using Lasso","date":"2023-05-17","arxiv_id":"2305.10413","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":"hinova-a-novel-open-set-detection-method-for","title":"HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication","date":"2023-05-16","arxiv_id":"2305.09594","repositories_listed":0,"syntology":null},{"url":null,"slug":"monitoring-multicountry-macroeconomic-risk","title":"Monitoring multicountry macroeconomic risk","date":"2023-05-16","arxiv_id":"2305.09563","repositories_listed":0,"syntology":null},{"url":null,"slug":"ortho-ode-enhancing-robustness-and-of-neural","title":"Ortho-ODE: Enhancing Robustness and of Neural ODEs against Adversarial Attacks","date":"2023-05-16","arxiv_id":"2305.09179","repositories_listed":0,"syntology":null},{"url":null,"slug":"ptse-a-multi-model-ensemble-method-for","title":"pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting","date":"2023-05-16","arxiv_id":"2305.11304","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-of-normal-and-abnormal-hearts","title":"Understanding of Normal and Abnormal Hearts by Phase Space Analysis and Convolutional Neural Networks","date":"2023-05-16","arxiv_id":"2305.10450","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-sequence-to-sequence-learning","title":"Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring","date":"2023-05-16","arxiv_id":"2305.09368","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-convolutional-fuzzy-time-series","title":"Differential Convolutional Fuzzy Time Series Forecasting","date":"2023-05-15","arxiv_id":"2305.08890","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-strategy-of-time-series-anomaly","title":"Evaluation Strategy of Time-series Anomaly Detection with Decay Function","date":"2023-05-15","arxiv_id":"2305.09691","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dataset-fusion-algorithm-for-generalised","title":"A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets","date":"2023-05-14","arxiv_id":"2305.08197","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiperformer-hierarchically-permutation","title":"HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting","date":"2023-05-14","arxiv_id":"2305.08073","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-unplanned-readmissions-in-the","title":"Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation","date":"2023-05-14","arxiv_id":"2305.08139","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-home-energy-management-vae-gan","title":"Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning","date":"2023-05-14","arxiv_id":"2305.08885","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generic-approach-to-integrating-time-into","title":"A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields","date":"2023-05-11","arxiv_id":"2305.06827","repositories_listed":0,"syntology":null},{"url":null,"slug":"band-pass-filtering-with-high-dimensional","title":"Band-Pass Filtering with High-Dimensional Time Series","date":"2023-05-11","arxiv_id":"2305.06618","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-of-time-varying-graphs-based-on","title":"Clustering of Time-Varying Graphs Based on Temporal Label Smoothness","date":"2023-05-11","arxiv_id":"2305.06576","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-expressive-are-spectral-temporal-graph","title":"How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?","date":"2023-05-11","arxiv_id":"2305.06587","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-detection-of-lead-lag-relationships-in","title":"Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models","date":"2023-05-11","arxiv_id":"2305.06704","repositories_listed":0,"syntology":null},{"url":null,"slug":"mispronunciation-detection-of-basic-quranic","title":"Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning","date":"2023-05-10","arxiv_id":"2305.06429","repositories_listed":0,"syntology":null},{"url":null,"slug":"xmi-icu-explainable-machine-learning-model","title":"XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients","date":"2023-05-10","arxiv_id":"2305.06109","repositories_listed":0,"syntology":null},{"url":null,"slug":"copula-variational-lstm-for-high-dimensional","title":"Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling","date":"2023-05-09","arxiv_id":"2305.08778","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-numerical-integration-in-rnn","title":"Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems","date":"2023-05-08","arxiv_id":"2305.04670","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-a-gradient-based-explainable-ai","title":"Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises","date":"2023-05-08","arxiv_id":"2305.05525","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlinear-rethink-the-linear-model-for-time","title":"Mlinear: Rethink the Linear Model for Time-series Forecasting","date":"2023-05-08","arxiv_id":"2305.04800","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-pattern-based-anomaly-detection-in","title":"Efficient pattern-based anomaly detection in a network of multivariate devices","date":"2023-05-07","arxiv_id":"2305.05538","repositories_listed":0,"syntology":null},{"url":null,"slug":"twin-support-vector-quantile-regression","title":"Twin support vector quantile regression","date":"2023-05-06","arxiv_id":"2305.03894","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":"blind-identification-of-ambisonic-reduced","title":"Blind identification of Ambisonic reduced room impulse response","date":"2023-05-05","arxiv_id":"2305.03558","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-discovery-with-stage-variables-for","title":"Causal Discovery with Stage Variables for Health Time Series","date":"2023-05-05","arxiv_id":"2305.03662","repositories_listed":0,"syntology":null},{"url":null,"slug":"si-lstm-speaker-hybrid-long-short-term-memory","title":"SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation","date":"2023-05-04","arxiv_id":"2305.03506","repositories_listed":0,"syntology":null},{"url":null,"slug":"wavelet-coherence-of-total-solar-irradiance","title":"Wavelet Coherence Of Total Solar Irradiance and Atlantic Climate","date":"2023-05-03","arxiv_id":"2305.02319","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecast-reconciliation-for-vaccine-supply","title":"Forecast reconciliation for vaccine supply chain optimization","date":"2023-05-02","arxiv_id":"2305.01455","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-short-term-probabilistic","title":"A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models","date":"2023-05-01","arxiv_id":"2305.00933","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-models-for-time-series-applications","title":"Diffusion Models for Time Series Applications: A Survey","date":"2023-05-01","arxiv_id":"2305.00624","repositories_listed":0,"syntology":null},{"url":null,"slug":"expanding-the-prediction-capacity-in-long","title":"Expanding the Prediction Capacity in Long Sequence Time-Series Forecasting","date":"2023-05-01","arxiv_id":null,"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":"impact-of-deep-learning-libraries-on-online","title":"Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection","date":"2023-04-30","arxiv_id":"2305.00595","repositories_listed":0,"syntology":null}],"record_sha256":"e342e3eb71bde18a7460f6874f92722c3dbeb5f02af8085087a6f43b19e0b4df","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}