{"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/22","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":22,"pages_in_order":68,"rows_per_page":100,"rows":[2101,2200],"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/21","next":"/task/time-series/papers/23","papers":[{"url":null,"slug":"an-end-to-end-model-for-time-series","title":"An End-to-End Model for Time Series Classification In the Presence of Missing Values","date":"2024-08-11","arxiv_id":"2408.05849","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-fine-grained-causality-in-climate","title":"Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection","date":"2024-08-08","arxiv_id":"2408.04254","repositories_listed":0,"syntology":null},{"url":null,"slug":"appformer-a-novel-framework-for-mobile-app","title":"Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction","date":"2024-07-28","arxiv_id":"2407.19414","repositories_listed":0,"syntology":null},{"url":null,"slug":"quadratic-advantage-with-quantum-randomized","title":"Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis","date":"2024-07-25","arxiv_id":"2407.18021","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-is-not-optimal-for","title":"Contrastive Learning Is Not Optimal for Quasiperiodic Time Series","date":"2024-07-24","arxiv_id":"2407.17073","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiervar-a-hierarchical-feature-selection","title":"HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis","date":"2024-07-22","arxiv_id":"2407.16048","repositories_listed":0,"syntology":null},{"url":null,"slug":"omni-dimensional-frequency-learner-for","title":"Omni-Dimensional Frequency Learner for General Time Series Analysis","date":"2024-07-15","arxiv_id":"2407.10419","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-bifurcation-method-for-observation","title":"A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System","date":"2024-07-06","arxiv_id":"2407.05182","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-sentinel-2-multi-band-imagery","title":"Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring","date":"2024-06-30","arxiv_id":"2407.00834","repositories_listed":0,"syntology":null},{"url":null,"slug":"filtration-learning-in-exact-multi-parameter","title":"Filtration learning in exact multi-parameter persistent homology and classification of time-series data","date":"2024-06-28","arxiv_id":"2406.19587","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-different-design-choices-in","title":"Understanding Different Design Choices in Training Large Time Series Models","date":"2024-06-20","arxiv_id":"2406.14045","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-seizure-detection-suitable","title":"Energy-Efficient Seizure Detection Suitable for low-power Applications","date":"2024-06-19","arxiv_id":"2406.16948","repositories_listed":0,"syntology":null},{"url":null,"slug":"game-of-llms-discovering-structural","title":"Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models","date":"2024-06-19","arxiv_id":"2406.13777","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-multivariate-time","title":"Data Augmentation for Multivariate Time Series Classification: An Experimental Study","date":"2024-06-10","arxiv_id":"2406.06518","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidentially-calibrated-source-free-time","title":"Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation","date":"2024-06-04","arxiv_id":"2406.02635","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-time-series-processing-for","title":"Efficient Time Series Processing for Transformers and State-Space Models through Token Merging","date":"2024-05-28","arxiv_id":"2405.17951","repositories_listed":0,"syntology":null},{"url":null,"slug":"unitnorm-rethinking-normalization-for","title":"UnitNorm: Rethinking Normalization for Transformers in Time Series","date":"2024-05-24","arxiv_id":"2405.15903","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-ffm-towards-lm-empowered-federated","title":"Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting","date":"2024-05-23","arxiv_id":"2405.14252","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-learning-framework-with-a-class","title":"An Active Learning Framework with a Class Balancing Strategy for Time Series Classification","date":"2024-05-20","arxiv_id":"2405.12122","repositories_listed":0,"syntology":null},{"url":null,"slug":"adawavenet-adaptive-wavelet-network-for-time","title":"AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis","date":"2024-05-17","arxiv_id":"2405.11124","repositories_listed":0,"syntology":null},{"url":null,"slug":"unicl-a-universal-contrastive-learning","title":"UniCL: A Universal Contrastive Learning Framework for Large Time Series Models","date":"2024-05-17","arxiv_id":"2405.10597","repositories_listed":0,"syntology":null},{"url":null,"slug":"weits-a-wavelet-enhanced-residual-framework","title":"WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting","date":"2024-05-17","arxiv_id":"2405.10877","repositories_listed":0,"syntology":null},{"url":null,"slug":"kolmogorov-arnold-networks-kans-for-time","title":"Kolmogorov-Arnold Networks (KANs) for Time Series Analysis","date":"2024-05-14","arxiv_id":"2405.08790","repositories_listed":0,"syntology":null},{"url":null,"slug":"ts3im-unveiling-structural-similarity-in-time","title":"TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights","date":"2024-05-10","arxiv_id":"2405.06234","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-generalization-analysis-to-optimization","title":"From Generalization Analysis to Optimization Designs for State Space Models","date":"2024-05-04","arxiv_id":"2405.02670","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-and-geometric-fusion-for","title":"Spatial, Temporal, and Geometric Fusion for Remote Sensing Images","date":"2024-04-27","arxiv_id":"2404.17851","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-potential-of-ai-for-spatially","title":"Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks","date":"2024-04-26","arxiv_id":"2404.17369","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-large-language-models-on-time","title":"Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark","date":"2024-04-25","arxiv_id":"2404.16563","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-of-data-centric-time-series-analysis","title":"Review of Data-centric Time Series Analysis from Sample, Feature, and Period","date":"2024-04-24","arxiv_id":"2404.16886","repositories_listed":0,"syntology":null},{"url":null,"slug":"timecsl-unsupervised-contrastive-learning-of","title":"TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis","date":"2024-04-07","arxiv_id":"2404.05057","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-satellite-image-time-series","title":"Deep Learning for Satellite Image Time Series Analysis: A Review","date":"2024-04-05","arxiv_id":"2404.03936","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-high-order-solver-for-signature-kernels","title":"Log-PDE Methods for Rough Signature Kernels","date":"2024-04-01","arxiv_id":"2404.02926","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-hypergraph-neural-networks-an-in","title":"A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide","date":"2024-04-01","arxiv_id":"2404.01039","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-deep-learning-and-state-of-the","title":"A Survey on State-of-the-art Deep Learning Applications and Challenges","date":"2024-03-26","arxiv_id":"2403.17561","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-informed-mathematical-model-of","title":"A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery","date":"2024-03-22","arxiv_id":"2403.15284","repositories_listed":0,"syntology":null},{"url":null,"slug":"capsule-neural-networks-as-noise-stabilizer","title":"Capsule Neural Networks as Noise Stabilizer for Time Series Data","date":"2024-03-20","arxiv_id":"2403.13867","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-multilingual-topic-dynamics-and","title":"Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models","date":"2024-03-18","arxiv_id":"2403.15445","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-multivariate-time-series-similarity","title":"Advancing multivariate time series similarity assessment: an integrated computational approach","date":"2024-03-16","arxiv_id":"2403.11044","repositories_listed":0,"syntology":null},{"url":null,"slug":"caformer-rethinking-time-series-analysis-from","title":"Caformer: Rethinking Time Series Analysis from Causal Perspective","date":"2024-03-13","arxiv_id":"2403.08572","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-analysis-of-key-societal-events","title":"Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams","date":"2024-03-11","arxiv_id":"2403.07090","repositories_listed":0,"syntology":null},{"url":null,"slug":"equipment-health-assessment-time-series","title":"Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance","date":"2024-03-01","arxiv_id":"2403.00975","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-analysis-in-compressor-based","title":"Time Series Analysis in Compressor-Based Machines: A Survey","date":"2024-02-27","arxiv_id":"2402.17802","repositories_listed":0,"syntology":null},{"url":null,"slug":"imuoptimize-a-data-driven-approach-to-optimal","title":"IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture","date":"2024-02-14","arxiv_id":"2402.08923","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-taylor-series-and-recursive","title":"Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction","date":"2024-02-09","arxiv_id":"2402.06441","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-vq-transformer-an-ffn-free-framework","title":"Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting","date":"2024-02-08","arxiv_id":"2402.05830","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-impact-of-distribution-shift-on","title":"Assessing the Impact of Distribution Shift on Reinforcement Learning Performance","date":"2024-02-05","arxiv_id":"2402.03590","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-time-series-analysis-with-large","title":"Empowering Time Series Analysis with Large Language Models: A Survey","date":"2024-02-05","arxiv_id":"2402.03182","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-fmri-time-series-analysis-for","title":"Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI","date":"2024-02-05","arxiv_id":"2402.02811","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-market-dynamics-unraveling","title":"Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis","date":"2024-02-04","arxiv_id":"2402.02623","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-observation-time-window","title":"Efficient Observation Time Window Segmentation for Administrative Data Machine Learning","date":"2024-01-29","arxiv_id":"2401.16537","repositories_listed":0,"syntology":null},{"url":null,"slug":"tnanet-a-temporal-noise-aware-neural-network","title":"TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data","date":"2024-01-23","arxiv_id":"2401.12733","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-bigger-the-better-rethinking-the","title":"Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting","date":"2024-01-22","arxiv_id":"2401.11929","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-to-detect-dynamical","title":"Machine learning approach to detect dynamical states from recurrence measures","date":"2024-01-18","arxiv_id":"2401.10298","repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-systems-approach-to-natural-language","title":"Complex systems approach to natural language","date":"2024-01-05","arxiv_id":"2401.02772","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-time-series-forecasting-towards","title":"Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach","date":"2023-12-31","arxiv_id":"2401.00534","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-domain-discrimination-for-multi","title":"POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning","date":"2023-12-19","arxiv_id":"2312.12276","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dynamic-triple-gamma-prior-as-a-shrinkage","title":"The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models","date":"2023-12-16","arxiv_id":"2312.10487","repositories_listed":0,"syntology":null},{"url":null,"slug":"dance-of-channel-and-sequence-an-efficient","title":"CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting","date":"2023-12-11","arxiv_id":"2312.06220","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-age-prediction-utilizing-lstm-based","title":"Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis","date":"2023-12-11","arxiv_id":"2312.08383","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-deep-neural-networks-to-improve-the","title":"Using deep neural networks to improve the precision of fast-sampled particle timing detectors","date":"2023-12-10","arxiv_id":"2312.05883","repositories_listed":0,"syntology":null},{"url":null,"slug":"signatures-meet-dynamic-programming","title":"Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following","date":"2023-12-09","arxiv_id":"2312.05547","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-pose-forecasting","title":"Personalized Pose Forecasting","date":"2023-12-06","arxiv_id":"2312.03528","repositories_listed":0,"syntology":null},{"url":null,"slug":"moe-amc-enhancing-automatic-modulation","title":"MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts","date":"2023-12-04","arxiv_id":"2312.02298","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-framework-for-generalizing","title":"An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments","date":"2023-11-30","arxiv_id":"2311.18824","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-time-online-visibility-graph","title":"Linear-time online visibility graph transformation algorithm: for both natural and horizontal visibility criteria","date":"2023-11-21","arxiv_id":"2311.12389","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-scale-network-a-shallow-neural-network","title":"Time Scale Network: A Shallow Neural Network For Time Series Data","date":"2023-11-10","arxiv_id":"2311.06170","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-opportunities-of-green-computing-a","title":"On the Opportunities of Green Computing: A Survey","date":"2023-11-01","arxiv_id":"2311.00447","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-for-transformer-based","title":"A Systematic Review for Transformer-based Long-term Series Forecasting","date":"2023-10-31","arxiv_id":"2310.20218","repositories_listed":0,"syntology":null},{"url":null,"slug":"triple-simplex-matrix-completion-for-expense","title":"Triple Simplex Matrix Completion for Expense Forecasting","date":"2023-10-23","arxiv_id":"2310.15275","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-early-warning-signals-of-systemic","title":"Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach","date":"2023-10-16","arxiv_id":"2310.10283","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-feature-types-and-their","title":"A Survey of Feature Types and Their Contributions for Camera Tampering Detection","date":"2023-10-11","arxiv_id":"2310.07886","repositories_listed":0,"syntology":null},{"url":null,"slug":"precise-localization-within-the-gi-tract-by","title":"Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs","date":"2023-10-11","arxiv_id":"2310.07895","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-time-series-analysis-approaches","title":"Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review","date":"2023-10-05","arxiv_id":"2310.03606","repositories_listed":0,"syntology":null},{"url":null,"slug":"noxtrader-lstm-based-stock-return-momentum","title":"NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading","date":"2023-10-01","arxiv_id":"2310.00747","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffeomorphic-transformations-for-time-series","title":"Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping","date":"2023-09-25","arxiv_id":"2309.14029","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-temperature-of-major-cities-using","title":"Predicting Temperature of Major Cities Using Machine Learning and Deep Learning","date":"2023-09-23","arxiv_id":"2309.13330","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-text-to-trends-a-unique-garden-analytics","title":"From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture","date":"2023-09-22","arxiv_id":"2309.12579","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-detection-analysis-for-temporal-memory","title":"A detection analysis for temporal memory patterns at different time-scales","date":"2023-09-21","arxiv_id":"2309.12034","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-beyond-similarities-incorporating","title":"Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning","date":"2023-09-14","arxiv_id":"2309.07526","repositories_listed":0,"syntology":null},{"url":null,"slug":"agriculture-credit-and-economic-growth-in","title":"Agriculture Credit and Economic Growth in Bangladesh: A Time Series Analysis","date":"2023-09-08","arxiv_id":"2309.04118","repositories_listed":0,"syntology":null},{"url":null,"slug":"physiozoo-the-open-digital-physiological","title":"PhysioZoo: The Open Digital Physiological Biomarkers Resource","date":"2023-09-07","arxiv_id":"2309.04498","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-analysis-of-urban-liveability","title":"Time Series Analysis of Urban Liveability","date":"2023-09-01","arxiv_id":"2309.00594","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-financial-market-trends-using-time","title":"Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing","date":"2023-08-31","arxiv_id":"2309.00136","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-time-series-forecasting-with","title":"Hierarchical Time Series Forecasting with Bayesian Modeling","date":"2023-08-28","arxiv_id":"2308.14719","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-time-series-regression","title":"High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods","date":"2023-08-27","arxiv_id":"2308.16192","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-signatures-for-seizure-forecasting","title":"Path Signatures for Seizure Forecasting","date":"2023-08-18","arxiv_id":"2308.09312","repositories_listed":0,"syntology":null},{"url":null,"slug":"distinguishing-risk-preferences-using","title":"Distinguishing Risk Preferences using Repeated Gambles","date":"2023-08-14","arxiv_id":"2308.07054","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-bayesian-context-trees-state-space-model","title":"The Bayesian Context Trees State Space Model for time series modelling and forecasting","date":"2023-08-02","arxiv_id":"2308.00913","repositories_listed":0,"syntology":null},{"url":null,"slug":"timepool-visually-answer-which-and-when","title":"TimePool: Visually Answer \"Which and When\" Questions On Univariate Time Series","date":"2023-08-01","arxiv_id":"2308.00682","repositories_listed":0,"syntology":null},{"url":null,"slug":"unraveling-the-complexity-of-splitting","title":"Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis","date":"2023-07-26","arxiv_id":"2307.14294","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-capturing-and-activation-of","title":"Forecasting, capturing and activation of carbon-dioxide (CO$_2$): Integration of Time Series Analysis, Machine Learning, and Material Design","date":"2023-07-25","arxiv_id":"2307.14374","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-shaped-transformer-retain-high-frequency","title":"U-shaped Transformer: Retain High Frequency Context in Time Series Analysis","date":"2023-07-18","arxiv_id":"2307.09019","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-time-series-characterization-and","title":"Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks","date":"2023-07-13","arxiv_id":"2307.06645","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-examination-of-wearable-sensors-and-video","title":"An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification","date":"2023-07-10","arxiv_id":"2307.04516","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-site-agnostic-multimodal-deep","title":"A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media","date":"2023-07-06","arxiv_id":"2307.06775","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-spatial-temporal-variations-of","title":"Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy","date":"2023-06-28","arxiv_id":"2306.16031","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-optimal-heteroscedastic-regression-with","title":"Near Optimal Heteroscedastic Regression with Symbiotic Learning","date":"2023-06-25","arxiv_id":"2306.14288","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-the-emotion-carriers-of-covid","title":"Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States","date":"2023-06-24","arxiv_id":"2306.13954","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-error-correction-mid-term-electricity-load","title":"An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition","date":"2023-06-19","arxiv_id":"2306.10826","repositories_listed":0,"syntology":null},{"url":null,"slug":"brainnet-epileptic-wave-detection-from-seeg","title":"BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning","date":"2023-06-15","arxiv_id":"2306.13101","repositories_listed":0,"syntology":null}],"record_sha256":"7c7a5e23bf92e5c9bc311a7bdb75b8e5359f7a399f68e357a0eb260a501410c8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}