{"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/52","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":52,"pages_in_order":92,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/task/time-series-1/papers/53","papers":[{"url":null,"slug":"new-bootstrap-tests-for-categorical-time","title":"New bootstrap tests for categorical time series. A comparative study","date":"2023-04-30","arxiv_id":"2305.00465","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-clustering-based-on-prediction","title":"Time series clustering based on prediction accuracy of global forecasting models","date":"2023-04-30","arxiv_id":"2305.00473","repositories_listed":0,"syntology":null},{"url":null,"slug":"industry-classification-using-a-novel","title":"Industry Classification Using a Novel Financial Time-Series Case Representation","date":"2023-04-29","arxiv_id":"2305.00245","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-neural-ordinary-differential-equations","title":"Using neural ordinary differential equations to predict complex ecological dynamics from population density data","date":"2023-04-29","arxiv_id":"2305.00338","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-text-mining-and-technical-analyses","title":"Assessing Text Mining and Technical Analyses on Forecasting Financial Time Series","date":"2023-04-27","arxiv_id":"2304.14544","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-adversarial-physical-attacks-in","title":"Detection of Adversarial Physical Attacks in Time-Series Image Data","date":"2023-04-27","arxiv_id":"2304.13919","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-assisted-bayesian-inference","title":"Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)","date":"2023-04-26","arxiv_id":"2304.13660","repositories_listed":0,"syntology":null},{"url":null,"slug":"common-correlated-effects-estimation-of","title":"Common Correlated Effects Estimation of Nonlinear Panel Data Models","date":"2023-04-25","arxiv_id":"2304.13199","repositories_listed":0,"syntology":null},{"url":null,"slug":"directed-chain-generative-adversarial","title":"Directed Chain Generative Adversarial Networks","date":"2023-04-25","arxiv_id":"2304.13131","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-temporal-analysis-of","title":"Self-Supervised Temporal Analysis of Spatiotemporal Data","date":"2023-04-25","arxiv_id":"2304.13143","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-categorical-time-series-with-the-r","title":"Analyzing categorical time series with the R package ctsfeatures","date":"2023-04-24","arxiv_id":"2304.12332","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-modelling-of-brain-activity-using","title":"Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator","date":"2023-04-24","arxiv_id":"2304.11925","repositories_listed":0,"syntology":null},{"url":null,"slug":"determination-of-the-effective-cointegration","title":"Determination of the effective cointegration rank in high-dimensional time-series predictive regressions","date":"2023-04-24","arxiv_id":"2304.12134","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-clustering-of-ordinal-time-series-based","title":"Fuzzy clustering of ordinal time series based on two novel distances with economic applications","date":"2023-04-24","arxiv_id":"2304.12249","repositories_listed":0,"syntology":null},{"url":null,"slug":"ordinal-time-series-analysis-with-the-r","title":"Ordinal time series analysis with the R package otsfeatures","date":"2023-04-24","arxiv_id":"2304.12251","repositories_listed":0,"syntology":null},{"url":null,"slug":"airbirds-a-large-scale-challenging-dataset","title":"AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports","date":"2023-04-23","arxiv_id":"2304.11662","repositories_listed":0,"syntology":null},{"url":null,"slug":"battery-capacity-knee-identification-using","title":"Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature","date":"2023-04-23","arxiv_id":"2304.11671","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cd-in-satellite-image-time","title":"Unsupervised CD in satellite image time series by contrastive learning and feature tracking","date":"2023-04-22","arxiv_id":"2304.11375","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-techniques-for-financial-time","title":"Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022","date":"2023-04-21","arxiv_id":"2305.04811","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-wind-power-forecast-precision-via","title":"Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting","date":"2023-04-21","arxiv_id":"2304.10758","repositories_listed":0,"syntology":null},{"url":null,"slug":"exogenous-data-in-forecasting-farm-an","title":"Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation","date":"2023-04-21","arxiv_id":"2304.11028","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecast-ergodicity-prediction-modeling-using","title":"Algorithmic Information Forecastability","date":"2023-04-21","arxiv_id":"2304.10752","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-based-lstm-autoencoder-for","title":"Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data","date":"2023-04-21","arxiv_id":"2305.09475","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attention-free-conditional-autoencoder-for","title":"An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies","date":"2023-04-20","arxiv_id":"2304.10614","repositories_listed":0,"syntology":null},{"url":null,"slug":"fruit-picker-activity-recognition-with","title":"Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning","date":"2023-04-20","arxiv_id":"2304.10068","repositories_listed":0,"syntology":null},{"url":null,"slug":"srel-severity-rating-ensemble-learning-for","title":"Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain","date":"2023-04-20","arxiv_id":"2304.10207","repositories_listed":0,"syntology":null},{"url":null,"slug":"zebra-z-order-curve-based-event-retrieval","title":"ZEBRA: Z-order Curve-based Event Retrieval Approach to Efficiently Explore Automotive Data","date":"2023-04-20","arxiv_id":"2304.10232","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-framework-for-traffic-data","title":"A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies","date":"2023-04-18","arxiv_id":"2304.09182","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoregressive-models-for-biomedical-signal","title":"Autoregressive models for biomedical signal processing","date":"2023-04-17","arxiv_id":"2304.11070","repositories_listed":0,"syntology":null},{"url":null,"slug":"obstacle-transformer-a-trajectory-prediction","title":"Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories","date":"2023-04-16","arxiv_id":"2304.07711","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-via-contextual","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","date":"2023-04-16","arxiv_id":"2304.07898","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-domain-adaptation-for-time","title":"Context-aware Domain Adaptation for Time Series Anomaly Detection","date":"2023-04-15","arxiv_id":"2304.07453","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-time-recurrent-neural-networks","title":"Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit","date":"2023-04-14","arxiv_id":"2304.07025","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-and-estimation-of-structural-breaks","title":"Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series","date":"2023-04-14","arxiv_id":"2304.07003","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-metro-deep-learning-approaches-to","title":"Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership","date":"2023-04-14","arxiv_id":"2304.07303","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-topological-data-analysis-detects","title":"Why Topological Data Analysis Detects Financial Bubbles?","date":"2023-04-14","arxiv_id":"2304.06877","repositories_listed":0,"syntology":null},{"url":null,"slug":"streamlined-framework-for-agile-forecasting","title":"Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management","date":"2023-04-13","arxiv_id":"2304.06344","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-alignment-using-unsupervised-learning","title":"Video alignment using unsupervised learning of local and global features","date":"2023-04-13","arxiv_id":"2304.06841","repositories_listed":0,"syntology":null},{"url":null,"slug":"np-free-a-real-time-normalization-free-and","title":"NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series","date":"2023-04-12","arxiv_id":"2304.06168","repositories_listed":0,"syntology":null},{"url":null,"slug":"electricity-demand-forecasting-with-hybrid","title":"Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine","date":"2023-04-11","arxiv_id":"2304.05174","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-time-series-forecasting-using-cnn","title":"Financial Time Series Forecasting using CNN and Transformer","date":"2023-04-11","arxiv_id":"2304.04912","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-modeling-for-time-series-via-schr","title":"Generative modeling for time series via Schr{ö}dinger bridge","date":"2023-04-11","arxiv_id":"2304.05093","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-fusion-fault-diagnosis-method","title":"Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios","date":"2023-04-11","arxiv_id":"2304.05198","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-frequency-co-movements-between","title":"Time-frequency co-movements between commodities and economic policy uncertainty across different crises","date":"2023-04-11","arxiv_id":"2304.05517","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-inspired-spiking-neural-network-for","title":"Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction","date":"2023-04-10","arxiv_id":"2304.04697","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-steps-forward-and-one-behind-rethinking","title":"Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning","date":"2023-04-10","arxiv_id":"2304.04553","repositories_listed":0,"syntology":null},{"url":null,"slug":"embarrassingly-simple-mixup-for-time-series","title":"Embarrassingly Simple MixUp for Time-series","date":"2023-04-09","arxiv_id":"2304.04271","repositories_listed":0,"syntology":null},{"url":null,"slug":"filling-out-the-missing-gaps-time-series","title":"Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning","date":"2023-04-09","arxiv_id":"2304.04275","repositories_listed":0,"syntology":null},{"url":null,"slug":"ofter-an-online-pipeline-for-time-series","title":"OFTER: An Online Pipeline for Time Series Forecasting","date":"2023-04-08","arxiv_id":"2304.03877","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-student-s-t-distribution-with-method","title":"Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series","date":"2023-04-06","arxiv_id":"2304.03069","repositories_listed":0,"syntology":null},{"url":null,"slug":"bitcoin-a-life-in-crises","title":"Bitcoin: A life in crises","date":"2023-04-06","arxiv_id":"2304.09939","repositories_listed":0,"syntology":null},{"url":null,"slug":"ezclone-improving-dnn-model-extraction-attack","title":"EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles","date":"2023-04-06","arxiv_id":"2304.03388","repositories_listed":0,"syntology":null},{"url":null,"slug":"spintronic-physical-reservoir-for-autonomous","title":"Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting","date":"2023-04-06","arxiv_id":"2304.03343","repositories_listed":0,"syntology":null},{"url":null,"slug":"ss-shapelets-semi-supervised-clustering-of","title":"SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets","date":"2023-04-06","arxiv_id":"2304.03292","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-time-series-person-and-soft","title":"Multi-Channel Time-Series Person and Soft-Biometric Identification","date":"2023-04-04","arxiv_id":"2304.01585","repositories_listed":0,"syntology":null},{"url":null,"slug":"side-channel-assisted-inference-leakage-from","title":"Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification","date":"2023-04-04","arxiv_id":"2304.01990","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-neural-networks-and-time-series-of","title":"Artificial neural networks and time series of counts: A class of nonlinear INGARCH models","date":"2023-04-03","arxiv_id":"2304.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-market-based-statistics-of-actual-returns","title":"Market-Based \"Actual\" Returns of Investors","date":"2023-04-02","arxiv_id":"2304.06466","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-tech-decoupling","title":"The Tech Decoupling","date":"2023-04-02","arxiv_id":"2304.00510","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-deep-learning-based-damage","title":"A robust deep learning-based damage identification approach for SHM considering missing data","date":"2023-03-31","arxiv_id":"2304.00040","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-factor-model-a-novel-approach-for-motion","title":"Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI","date":"2023-03-31","arxiv_id":"2304.00102","repositories_listed":0,"syntology":null},{"url":null,"slug":"never-a-dull-moment-distributional-properties","title":"Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification","date":"2023-03-31","arxiv_id":"2303.17809","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-based-on","title":"Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces","date":"2023-03-31","arxiv_id":"2303.17802","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evaluation-of-time-series-forecasting","title":"An evaluation of time series forecasting models on water consumption data: A case study of Greece","date":"2023-03-30","arxiv_id":"2303.17617","repositories_listed":0,"syntology":null},{"url":null,"slug":"patterns-detection-in-glucose-time-series-by","title":"Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning","date":"2023-03-30","arxiv_id":"2303.17616","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-byzantine-resilient-aggregation-scheme-for","title":"Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection","date":"2023-03-29","arxiv_id":"2303.16668","repositories_listed":0,"syntology":null},{"url":"/paper/who-you-play-affects-how-you-play-predicting","slug":"who-you-play-affects-how-you-play-predicting","title":"Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution","date":"2023-03-29","arxiv_id":"2303.16741","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multifractional-option-pricing-formula","title":"A multifractional option pricing formula","date":"2023-03-28","arxiv_id":"2303.16314","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-private-to-public-benchmarking-gans-in","title":"From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification","date":"2023-03-28","arxiv_id":"2303.15916","repositories_listed":0,"syntology":null},{"url":"/paper/core-sleep-a-multimodal-fusion-framework-for","slug":"core-sleep-a-multimodal-fusion-framework-for","title":"CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities","date":"2023-03-27","arxiv_id":"2304.06485","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-black-box-parameter-estimation","title":"Towards black-box parameter estimation","date":"2023-03-27","arxiv_id":"2303.15041","repositories_listed":0,"syntology":null},{"url":null,"slug":"driver-profiling-and-bayesian-workload","title":"Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data","date":"2023-03-26","arxiv_id":"2303.14720","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-engineering-methods-on-multivariate","title":"Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions","date":"2023-03-26","arxiv_id":"2303.16117","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexity-calibrated-benchmarks-for-machine","title":"Complexity-calibrated Benchmarks for Machine Learning Reveal When Next-Generation Reservoir Computer Predictions Succeed and Mislead","date":"2023-03-25","arxiv_id":"2303.14553","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-synthetic-control","title":"Differentially Private Synthetic Control","date":"2023-03-24","arxiv_id":"2303.14084","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-is-all-connected-a-new-graph-formulation","title":"It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting","date":"2023-03-23","arxiv_id":"2303.13177","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-unidirectional-coupling-using-echo","title":"Learning unidirectional coupling using echo-state network","date":"2023-03-23","arxiv_id":"2303.13562","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-aeronautics-data-with","title":"Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model","date":"2023-03-22","arxiv_id":"2303.12302","repositories_listed":0,"syntology":null},{"url":null,"slug":"tsshap-robust-model-agnostic-feature-based","title":"TsSHAP: Robust model agnostic feature-based explainability for time series forecasting","date":"2023-03-22","arxiv_id":"2303.12316","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-adversarial-examples-on","title":"Wasserstein Adversarial Examples on Univariant Time Series Data","date":"2023-03-22","arxiv_id":"2303.12357","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-organization-in-the-human-brain","title":"Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy","date":"2023-03-21","arxiv_id":"2303.11994","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-asymptotic-pointwise-and-worst-case","title":"Non-Asymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators","date":"2023-03-21","arxiv_id":"2303.11908","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-miner-find-significant-and-stable","title":"Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning","date":"2023-03-21","arxiv_id":"2303.11716","repositories_listed":0,"syntology":null},{"url":null,"slug":"late-meta-learning-fusion-using","title":"Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting","date":"2023-03-20","arxiv_id":"2303.11000","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-cnn-rnn-approach-for-survival","title":"A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study","date":"2023-03-19","arxiv_id":"2303.10789","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-cross-correlation-estimates-from","title":"Optimal Cross-Correlation Estimates from Asynchronous Tick-by-Tick Trading Data","date":"2023-03-18","arxiv_id":"2303.16153","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-optimized-model-on-money-markets","title":"Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio","date":"2023-03-18","arxiv_id":"2303.10481","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bi-lstm-autoencoder-framework-for-anomaly","title":"A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset","date":"2023-03-17","arxiv_id":"2303.09703","repositories_listed":0,"syntology":null},{"url":null,"slug":"botshape-a-novel-social-bots-detection","title":"BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns","date":"2023-03-17","arxiv_id":"2303.10214","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-discovery-from-temporal-data-an","title":"Causal Discovery from Temporal Data: An Overview and New Perspectives","date":"2023-03-17","arxiv_id":"2303.10112","repositories_listed":0,"syntology":null},{"url":null,"slug":"tkn-transformer-based-keypoint-prediction","title":"TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction","date":"2023-03-17","arxiv_id":"2303.09807","repositories_listed":0,"syntology":null},{"url":null,"slug":"arbitrary-order-meta-learning-with-simple","title":"Arbitrary Order Meta-Learning with Simple Population-Based Evolution","date":"2023-03-16","arxiv_id":"2303.09478","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-synthetic-multi-dimensional","title":"Generating synthetic multi-dimensional molecular-mediator time series data for artificial intelligence-based disease trajectory forecasting and drug development digital twins: Considerations","date":"2023-03-16","arxiv_id":"2303.09056","repositories_listed":0,"syntology":null},{"url":null,"slug":"entropy-of-financial-time-series-due-to-the","title":"Entropy of financial time series due to the shock of war","date":"2023-03-15","arxiv_id":"2303.16155","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-covid-19-infections-in-gulf","title":"Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning","date":"2023-03-14","arxiv_id":"2303.07600","repositories_listed":0,"syntology":null},{"url":null,"slug":"fptn-fast-pure-transformer-network-for","title":"FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting","date":"2023-03-14","arxiv_id":"2303.07685","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sampling-designs-for-multi","title":"Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data","date":"2023-03-14","arxiv_id":"2303.08242","repositories_listed":0,"syntology":null},{"url":null,"slug":"sinkhorn-flow-predicting-probability-mass","title":"Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport","date":"2023-03-14","arxiv_id":"2303.07675","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-model-complexity-for-temporal","title":"Deep incremental learning models for financial temporal tabular datasets with distribution shifts","date":"2023-03-14","arxiv_id":"2303.07925","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-statistical-and-machine-learning","title":"Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study","date":"2023-03-13","arxiv_id":"2303.07139","repositories_listed":0,"syntology":null}],"record_sha256":"67afe10b74635366803b3202afb4722cbfb9b4c13c5834fde109d1967f36f4bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}