{"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/31","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":31,"pages_in_order":92,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/task/time-series-1/papers/32","papers":[{"url":null,"slug":"few-shot-learning-for-industrial-time-series","title":"Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring","date":"2025-06-16","arxiv_id":"2506.13909","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimus-observing-persistent-transformations","title":"OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data","date":"2025-06-16","arxiv_id":"2506.13902","repositories_listed":0,"syntology":null},{"url":null,"slug":"spacetrack-timeseries-time-series-dataset","title":"SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis","date":"2025-06-16","arxiv_id":"2506.13034","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-temperature-informed-sampling","title":"Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning","date":"2025-06-15","arxiv_id":"2506.12885","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-cross-validation-impacts","title":"Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation","date":"2025-06-13","arxiv_id":"2506.12183","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-time-series-foundation-models-perform","title":"Can Time-Series Foundation Models Perform Building Energy Management Tasks?","date":"2025-06-12","arxiv_id":"2506.11250","repositories_listed":0,"syntology":null},{"url":"/paper/time-imm-a-dataset-and-benchmark-for","slug":"time-imm-a-dataset-and-benchmark-for","title":"Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series","date":"2025-06-12","arxiv_id":"2506.10412","repositories_listed":0,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/time-imm-a-dataset-and-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2506.10412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.10412"}},"official":null}},{"url":null,"slug":"enhancing-bagging-ensemble-regression-with","title":"Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction","date":"2025-06-11","arxiv_id":"2506.13786","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-graph-recovery-in-neuroimaging-through","title":"Causal Graph Recovery in Neuroimaging through Answer Set Programming","date":"2025-06-10","arxiv_id":"2506.09286","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-based-time-series-forecasting-for","title":"Diffusion-based Time Series Forecasting for Sewerage Systems","date":"2025-06-10","arxiv_id":"2506.08577","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-representations-for","title":"Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers","date":"2025-06-10","arxiv_id":"2506.08641","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-real-world-multivariate-time","title":"Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness","date":"2025-06-10","arxiv_id":"2506.08660","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08113","title":"Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting","date":"2025-06-09","arxiv_id":"2506.08113","repositories_listed":0,"syntology":null},{"url":null,"slug":"mira-medical-time-series-foundation-model-for","title":"MIRA: Medical Time Series Foundation Model for Real-World Health Data","date":"2025-06-09","arxiv_id":"2506.07584","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-tvnet-a-long-term-time-series-prediction","title":"MS-TVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Dynamic Convolution","date":"2025-06-08","arxiv_id":"2506.17253","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cautious-user-s-guide-in-applying-hmms-to","title":"A cautious user's guide in applying HMMs to physical systems","date":"2025-06-06","arxiv_id":"2506.05707","repositories_listed":0,"syntology":null},{"url":null,"slug":"devicescope-an-interactive-app-to-detect-and","title":"DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series","date":"2025-06-06","arxiv_id":"2506.05912","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-unlock-time-series-editing-diffusion","title":"How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control","date":"2025-06-05","arxiv_id":"2506.05276","repositories_listed":0,"syntology":null},{"url":"/paper/neural-mjd-neural-non-stationary-merton-jump","slug":"neural-mjd-neural-non-stationary-merton-jump","title":"Neural MJD: Neural Non-Stationary Merton Jump Diffusion for Time Series Prediction","date":"2025-06-05","arxiv_id":"2506.04542","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-mjd-neural-non-stationary-merton-jump#ran","syntology_url":"https://syntology.ai/paper/2506.04542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.04542"}},"official":null}},{"url":null,"slug":"trace-contrastive-learning-for-multi-trial","title":"TRACE: Contrastive learning for multi-trial time-series data in neuroscience","date":"2025-06-05","arxiv_id":"2506.04906","repositories_listed":0,"syntology":null},{"url":null,"slug":"chime-conditional-hallucination-and","title":"CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model","date":"2025-06-04","arxiv_id":"2506.03502","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-mapping-from-static-labels-remote","title":"Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding","date":"2025-06-03","arxiv_id":"2506.02574","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-necessity-of-multi-domain-explanation","title":"On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models","date":"2025-06-03","arxiv_id":"2506.03267","repositories_listed":0,"syntology":null},{"url":null,"slug":"stock-market-telepathy-graph-neural-networks","title":"Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries","date":"2025-06-02","arxiv_id":"2506.01945","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-variational-implicit-neural","title":"Temporal Variational Implicit Neural Representations","date":"2025-06-02","arxiv_id":"2506.01544","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-promise-of-spiking-neural-networks-for","title":"The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives","date":"2025-06-02","arxiv_id":"2506.01737","repositories_listed":0,"syntology":null},{"url":null,"slug":"trojan-horse-hunt-in-time-series-forecasting","title":"Trojan Horse Hunt in Time Series Forecasting for Space Operations","date":"2025-06-02","arxiv_id":"2506.01849","repositories_listed":0,"syntology":null},{"url":"/paper/tsrating-rating-quality-of-diverse-time","slug":"tsrating-rating-quality-of-diverse-time","title":"TSRating: Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment","date":"2025-06-02","arxiv_id":"2506.01290","repositories_listed":0,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tsrating-rating-quality-of-diverse-time#ran","syntology_url":"https://syntology.ai/paper/2506.01290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.01290"}},"official":null}},{"url":null,"slug":"explainable-ai-powered-stock-price-prediction","title":"Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100","date":"2025-06-01","arxiv_id":"2506.06345","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-family-of-robust-generalized-adaptive","title":"A Family of Robust Generalized Adaptive Filters and Application for Time-series Prediction","date":"2025-05-31","arxiv_id":"2506.00397","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-imposed-fusion-a-simple-yet-effective","title":"Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification","date":"2025-05-31","arxiv_id":"2506.00337","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-growth-at-risk","title":"Machine-learning Growth at Risk","date":"2025-05-31","arxiv_id":"2506.00572","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-cumulative-encoding-meets-time-series","title":"Binary Cumulative Encoding meets Time Series Forecasting","date":"2025-05-30","arxiv_id":"2505.24595","repositories_listed":0,"syntology":null},{"url":"/paper/cluster-aware-causal-mixer-for-online-anomaly","slug":"cluster-aware-causal-mixer-for-online-anomaly","title":"Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series","date":"2025-05-30","arxiv_id":"2506.00188","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cluster-aware-causal-mixer-for-online-anomaly#ran","syntology_url":"https://syntology.ai/paper/2506.00188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.00188"}},"official":null}},{"url":null,"slug":"from-images-to-signals-are-large-vision","title":"From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?","date":"2025-05-29","arxiv_id":"2505.24030","repositories_listed":0,"syntology":null},{"url":"/paper/improving-time-series-forecasting-via","slug":"improving-time-series-forecasting-via","title":"Improving Time Series Forecasting via Instance-aware Post-hoc Revision","date":"2025-05-29","arxiv_id":"2505.23583","repositories_listed":0,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/improving-time-series-forecasting-via#ran","syntology_url":"https://syntology.ai/paper/2505.23583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.23583"}},"official":null}},{"url":"/paper/less-is-more-unlocking-specialization-of-time","slug":"less-is-more-unlocking-specialization-of-time","title":"Less is More: Unlocking Specialization of Time Series Foundation Models via Structured Pruning","date":"2025-05-29","arxiv_id":"2505.23195","repositories_listed":0,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/less-is-more-unlocking-specialization-of-time#ran","syntology_url":"https://syntology.ai/paper/2505.23195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.23195"}},"official":null}},{"url":null,"slug":"multi-modal-view-enhanced-large-vision-models","title":"Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting","date":"2025-05-29","arxiv_id":"2505.24003","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explainable-sequential-learning","title":"Towards Explainable Sequential Learning","date":"2025-05-29","arxiv_id":"2505.23624","repositories_listed":0,"syntology":null},{"url":null,"slug":"trajectory-generator-matching-for-time-series","title":"Trajectory Generator Matching for Time Series","date":"2025-05-29","arxiv_id":"2505.23215","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-evolution-pool-taming-recurring","title":"Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting","date":"2025-05-28","arxiv_id":"2506.14790","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-learning-for-proactive-fault","title":"Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures","date":"2025-05-27","arxiv_id":"2505.20705","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-time-series-foundation-models-deployment","title":"Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains","date":"2025-05-26","arxiv_id":"2505.19397","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-expected-signatures-theory-and","title":"Learning with Expected Signatures: Theory and Applications","date":"2025-05-26","arxiv_id":"2505.20465","repositories_listed":0,"syntology":null},{"url":null,"slug":"vista-vision-language-inference-for-training","title":"VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis","date":"2025-05-24","arxiv_id":"2505.18570","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperimts-hypergraph-neural-network-for","title":"HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting","date":"2025-05-23","arxiv_id":"2505.17431","repositories_listed":0,"syntology":null},{"url":"/paper/transdf-time-series-forecasting-needs","slug":"transdf-time-series-forecasting-needs","title":"TransDF: Time-Series Forecasting Needs Transformed Label Alignment","date":"2025-05-23","arxiv_id":"2505.17847","repositories_listed":0,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/transdf-time-series-forecasting-needs#ran","syntology_url":"https://syntology.ai/paper/2505.17847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.17847"}},"official":null}},{"url":null,"slug":"caiformer-a-causal-informed-transformer-for","title":"CAIFormer: A Causal Informed Transformer for Multivariate Time Series Forecasting","date":"2025-05-22","arxiv_id":"2505.16308","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-distributional-dynamics-for","title":"Analysis of Distributional Dynamics for Repeated Cross-Sectional and Intra-Period Observations","date":"2025-05-21","arxiv_id":"2505.15763","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-methods-of-matrix-valued-ar-model","title":"Estimation methods of Matrix-valued AR model","date":"2025-05-21","arxiv_id":"2505.15220","repositories_listed":0,"syntology":null},{"url":null,"slug":"forging-time-series-with-language-a-large","title":"Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation","date":"2025-05-21","arxiv_id":"2505.17103","repositories_listed":0,"syntology":null},{"url":null,"slug":"fr-mamba-time-series-physical-field","title":"FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model","date":"2025-05-21","arxiv_id":"2505.16083","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-for-time-series","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","date":"2025-05-21","arxiv_id":"2506.11040","repositories_listed":0,"syntology":null},{"url":null,"slug":"motime-a-dataset-suite-for-multimodal-time","title":"MoTime: A Dataset Suite for Multimodal Time Series Forecasting","date":"2025-05-21","arxiv_id":"2505.15072","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-tracker-mixture-of-experts-enhanced","title":"Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines","date":"2025-05-21","arxiv_id":"2505.15151","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-system-identification-approach-to","title":"A system identification approach to clustering vector autoregressive time series","date":"2025-05-20","arxiv_id":"2505.14421","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-stable-distribution-and-hurst","title":"Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series","date":"2025-05-20","arxiv_id":"2506.05354","repositories_listed":0,"syntology":null},{"url":null,"slug":"assimilative-causal-inference","title":"Assimilative Causal Inference","date":"2025-05-20","arxiv_id":"2505.14825","repositories_listed":0,"syntology":null},{"url":null,"slug":"byte-pair-encoding-for-efficient-time-series","title":"Byte Pair Encoding for Efficient Time Series Forecasting","date":"2025-05-20","arxiv_id":"2505.14411","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-and-limitations-in-the-synthetic","title":"Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data","date":"2025-05-20","arxiv_id":"2505.14206","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multivariate-long-term-history","title":"Leveraging Multivariate Long-Term History Representation for Time Series Forecasting","date":"2025-05-20","arxiv_id":"2505.14737","repositories_listed":0,"syntology":null},{"url":null,"slug":"msdformer-multi-scale-discrete-transformer","title":"MSDformer: Multi-scale Discrete Transformer For Time Series Generation","date":"2025-05-20","arxiv_id":"2505.14202","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-embedding-models-can-be-great-data","title":"Text embedding models can be great data engineers","date":"2025-05-20","arxiv_id":"2505.14802","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-similarity-score-functions-to","title":"Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs","date":"2025-05-20","arxiv_id":"2505.14739","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-to-embed-unlocking-foundation-models-for","title":"Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions","date":"2025-05-20","arxiv_id":"2505.14543","repositories_listed":0,"syntology":null},{"url":null,"slug":"cats-clustering-aggregated-and-time-series","title":"CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction","date":"2025-05-19","arxiv_id":"2505.13558","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-channel-independent-time-series","title":"Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding","date":"2025-05-19","arxiv_id":"2505.12761","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-llms-for-time-series-forecasting","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","date":"2025-05-19","arxiv_id":"2505.13175","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-based-anomalous-rssi","title":"Graph Neural Networks Based Anomalous RSSI Detection","date":"2025-05-19","arxiv_id":"2505.15847","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-representations-for-evolving","title":"Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)","date":"2025-05-19","arxiv_id":"2505.12806","repositories_listed":0,"syntology":null},{"url":null,"slug":"level-generation-with-quantum-reservoir","title":"Level Generation with Quantum Reservoir Computing","date":"2025-05-19","arxiv_id":"2505.13287","repositories_listed":0,"syntology":null},{"url":null,"slug":"resw-vl-representation-learning-for-surgical","title":"ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model","date":"2025-05-19","arxiv_id":"2505.13746","repositories_listed":0,"syntology":null},{"url":null,"slug":"true-zero-shot-inference-of-dynamical-systems","title":"True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics","date":"2025-05-19","arxiv_id":"2505.13192","repositories_listed":0,"syntology":null},{"url":null,"slug":"tspulse-dual-space-tiny-pre-trained-models","title":"TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis","date":"2025-05-19","arxiv_id":"2505.13033","repositories_listed":0,"syntology":null},{"url":null,"slug":"alternators-with-noise-models","title":"Alternators With Noise Models","date":"2025-05-18","arxiv_id":"2505.12544","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-order-wavelet-derivative-transform-for","title":"Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting","date":"2025-05-17","arxiv_id":"2505.11781","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10774","title":"Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting","date":"2025-05-16","arxiv_id":"2505.10774","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11106","title":"Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series","date":"2025-05-16","arxiv_id":"2505.11106","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11243","title":"A Set-Sequence Model for Time Series","date":"2025-05-16","arxiv_id":"2505.11243","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11250","title":"Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline","date":"2025-05-16","arxiv_id":"2505.11250","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11306","title":"Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion","date":"2025-05-16","arxiv_id":"2505.11306","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11321","title":"Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network","date":"2025-05-16","arxiv_id":"2505.11321","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11349","title":"Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning","date":"2025-05-16","arxiv_id":"2505.11349","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-time-cross-dimensional-frequency","title":"Beyond Time: Cross-Dimensional Frequency Supervision for Time Series Forecasting","date":"2025-05-16","arxiv_id":"2505.11567","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-network-anomaly-detection-with","title":"Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series","date":"2025-05-16","arxiv_id":"2505.11631","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-time-series-ai-model-for-realized","title":"Foundation Time-Series AI Model for Realized Volatility Forecasting","date":"2025-05-16","arxiv_id":"2505.11163","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearest-neighbor-multivariate-time-series","title":"Nearest Neighbor Multivariate Time Series Forecasting","date":"2025-05-16","arxiv_id":"2505.11625","repositories_listed":0,"syntology":null},{"url":null,"slug":"chronosteer-bridging-large-language-model-and","title":"ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data","date":"2025-05-15","arxiv_id":"2505.10083","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-scaling-law-apply-in-time-series","title":"Does Scaling Law Apply in Time Series Forecasting?","date":"2025-05-15","arxiv_id":"2505.10172","repositories_listed":0,"syntology":null},{"url":null,"slug":"informed-forecasting-leveraging-auxiliary","title":"Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting","date":"2025-05-15","arxiv_id":"2505.10213","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scale-representation-learning","title":"A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting","date":"2025-05-13","arxiv_id":"2505.08199","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-assisted-model-predictive-control","title":"Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation","date":"2025-05-13","arxiv_id":"2505.08535","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-simplification-algorithms-for","title":"Evaluating Simplification Algorithms for Interpretability of Time Series Classification","date":"2025-05-13","arxiv_id":"2505.08846","repositories_listed":0,"syntology":null},{"url":null,"slug":"spat-sensitivity-based-multihead-attention","title":"SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models","date":"2025-05-13","arxiv_id":"2505.08768","repositories_listed":0,"syntology":null},{"url":null,"slug":"4tastic-time-and-trend-traveling-time-series","title":"4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients","date":"2025-05-12","arxiv_id":"2505.07702","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-view-of-time-series-imputation-some","title":"Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism","date":"2025-05-12","arxiv_id":"2505.07180","repositories_listed":0,"syntology":null},{"url":null,"slug":"delphyne-a-pre-trained-model-for-general-and","title":"DELPHYNE: A Pre-Trained Model for General and Financial Time Series","date":"2025-05-12","arxiv_id":"2506.06288","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-the-excess-volatility-puzzle","title":"Revisiting the Excess Volatility Puzzle Through the Lens of the Chiarella Model","date":"2025-05-12","arxiv_id":"2505.07820","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-time-series-forecasting-via-a","title":"Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers","date":"2025-05-11","arxiv_id":"2505.06874","repositories_listed":0,"syntology":null},{"url":null,"slug":"newsnet-sdf-stochastic-discount-factor","title":"NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks","date":"2025-05-11","arxiv_id":"2505.06864","repositories_listed":0,"syntology":null}],"record_sha256":"03d5a8484553423891439581e0eb365e0f8c24300c27d6b69725ad08730a718f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}