{"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/9","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":9,"pages_in_order":92,"rows_per_page":100,"rows":[801,900],"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/8","next":"/task/time-series-1/papers/10","papers":[{"url":"/paper/ada-mshyper-adaptive-multi-scale-hypergraph","slug":"ada-mshyper-adaptive-multi-scale-hypergraph","title":"Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting","date":"2024-10-31","arxiv_id":"2410.23992","repositories_listed":1,"syntology":{"n":4,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"0 ran · 4 unverified","sample_list":"/paper/ada-mshyper-adaptive-multi-scale-hypergraph#ran","syntology_url":"https://syntology.ai/paper/2410.23992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23992"}},"official":{"repos":["shangzongjiang/Ada-MSHyper"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":[]}}},{"url":"/paper/ar-pro-counterfactual-explanations-for","slug":"ar-pro-counterfactual-explanations-for","title":"AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties","date":"2024-10-31","arxiv_id":"2410.24178","repositories_listed":1,"syntology":null},{"url":"/paper/flextsf-a-universal-forecasting-model-for","slug":"flextsf-a-universal-forecasting-model-for","title":"FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities","date":"2024-10-30","arxiv_id":"2410.23160","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-free-training-of-recurrent-neural","slug":"gradient-free-training-of-recurrent-neural","title":"Gradient-free training of recurrent neural networks","date":"2024-10-30","arxiv_id":"2410.23467","repositories_listed":1,"syntology":null},{"url":"/paper/mixad-memory-induced-explainable-time-series","slug":"mixad-memory-induced-explainable-time-series","title":"MIXAD: Memory-Induced Explainable Time Series Anomaly Detection","date":"2024-10-30","arxiv_id":"2410.22735","repositories_listed":1,"syntology":null},{"url":"/paper/tangent-space-causal-inference-leveraging","slug":"tangent-space-causal-inference-leveraging","title":"Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems","date":"2024-10-30","arxiv_id":"2410.23499","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tangent-space-causal-inference-leveraging#ran","syntology_url":"https://syntology.ai/paper/2410.23499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23499"}},"official":{"repos":["KurtButler/tangentspaces"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/waverora-wavelet-rotary-route-attention-for","slug":"waverora-wavelet-rotary-route-attention-for","title":"WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting","date":"2024-10-30","arxiv_id":"2410.22649","repositories_listed":1,"syntology":null},{"url":"/paper/a-temporal-linear-network-for-time-series","slug":"a-temporal-linear-network-for-time-series","title":"A Temporal Linear Network for Time Series Forecasting","date":"2024-10-28","arxiv_id":"2410.21448","repositories_listed":1,"syntology":null},{"url":"/paper/introducing-spectral-attention-for-long-range","slug":"introducing-spectral-attention-for-long-range","title":"Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting","date":"2024-10-28","arxiv_id":"2410.20772","repositories_listed":1,"syntology":null},{"url":"/paper/seriesgan-time-series-generation-via","slug":"seriesgan-time-series-generation-via","title":"SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning","date":"2024-10-28","arxiv_id":"2410.21203","repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-flow-matching-with-applications-to","slug":"trajectory-flow-matching-with-applications-to","title":"Trajectory Flow Matching with Applications to Clinical Time Series Modeling","date":"2024-10-28","arxiv_id":"2410.21154","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/trajectory-flow-matching-with-applications-to#ran","syntology_url":"https://syntology.ai/paper/2410.21154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21154"}},"official":{"repos":["nzhangx/trajectoryflowmatching"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/papagei-open-foundation-models-for-optical","slug":"papagei-open-foundation-models-for-optical","title":"PaPaGei: Open Foundation Models for Optical Physiological Signals","date":"2024-10-27","arxiv_id":"2410.20542","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/papagei-open-foundation-models-for-optical#ran","syntology_url":"https://syntology.ai/paper/2410.20542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20542"}},"official":{"repos":["nokia-bell-labs/papagei-foundation-model"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-battery-storage-energy-arbitrage","slug":"enhancing-battery-storage-energy-arbitrage","title":"Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting","date":"2024-10-25","arxiv_id":"2410.20005","repositories_listed":1,"syntology":null},{"url":"/paper/pmm-net-single-stage-multi-agent-trajectory","slug":"pmm-net-single-stage-multi-agent-trajectory","title":"PMM-Net: Single-stage Multi-agent Trajectory Prediction with Patching-based Embedding and Explicit Modal Modulation","date":"2024-10-25","arxiv_id":"2410.19544","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-image-transforms-and-diffusion","slug":"utilizing-image-transforms-and-diffusion","title":"Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series","date":"2024-10-25","arxiv_id":"2410.19538","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/utilizing-image-transforms-and-diffusion#ran","syntology_url":"https://syntology.ai/paper/2410.19538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.19538"}},"official":null}},{"url":"/paper/context-is-key-a-benchmark-for-forecasting","slug":"context-is-key-a-benchmark-for-forecasting","title":"Context is Key: A Benchmark for Forecasting with Essential Textual Information","date":"2024-10-24","arxiv_id":"2410.18959","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/context-is-key-a-benchmark-for-forecasting#ran","syntology_url":"https://syntology.ai/paper/2410.18959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.18959"}},"official":{"repos":["servicenow/context-is-key-forecasting"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-models-for-financial-aid-in","slug":"large-language-models-for-financial-aid-in","title":"Large Language Models for Financial Aid in Financial Time-series Forecasting","date":"2024-10-24","arxiv_id":"2410.19025","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-diffusion-models-for-time","slug":"retrieval-augmented-diffusion-models-for-time","title":"Retrieval-Augmented Diffusion Models for Time Series Forecasting","date":"2024-10-24","arxiv_id":"2410.18712","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/retrieval-augmented-diffusion-models-for-time#ran","syntology_url":"https://syntology.ai/paper/2410.18712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.18712"}},"official":{"repos":["stanliu96/RATD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/team-topological-evolution-aware-framework","slug":"team-topological-evolution-aware-framework","title":"TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version","date":"2024-10-24","arxiv_id":"2410.19192","repositories_listed":1,"syntology":{"n":20,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":20,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/team-topological-evolution-aware-framework#ran","syntology_url":"https://syntology.ai/paper/2410.19192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.19192"}},"official":{"repos":["kvmduc/TEAM-topo-evo-traffic-forecasting"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-deep-learning-for-time-series","slug":"leveraging-deep-learning-for-time-series","title":"Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars","date":"2024-10-23","arxiv_id":"2410.17906","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-time-series-a","slug":"self-supervised-learning-for-time-series-a","title":"Self-Supervised Learning for Time Series: A Review & Critique of FITS","date":"2024-10-23","arxiv_id":"2410.18318","repositories_listed":1,"syntology":null},{"url":"/paper/zeitenwenden-detecting-changes-in-the-german","slug":"zeitenwenden-detecting-changes-in-the-german","title":"Zeitenwenden: Detecting changes in the German political discourse","date":"2024-10-23","arxiv_id":"2410.17960","repositories_listed":1,"syntology":null},{"url":"/paper/lino-advancing-recursive-residual","slug":"lino-advancing-recursive-residual","title":"LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting","date":"2024-10-22","arxiv_id":"2410.17159","repositories_listed":1,"syntology":null},{"url":"/paper/xlstm-mixer-multivariate-time-series","slug":"xlstm-mixer-multivariate-time-series","title":"xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories","date":"2024-10-22","arxiv_id":"2410.16928","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"7 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/xlstm-mixer-multivariate-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.16928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.16928"}},"official":{"repos":["mauricekraus/xlstm-mixer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/data-matters-the-case-of-predicting-mobile","slug":"data-matters-the-case-of-predicting-mobile","title":"Data Matters: The Case of Predicting Mobile Cellular Traffic","date":"2024-10-21","arxiv_id":"2411.02418","repositories_listed":1,"syntology":null},{"url":"/paper/limtr-time-series-motion-prediction-for","slug":"limtr-time-series-motion-prediction-for","title":"LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration","date":"2024-10-21","arxiv_id":"2410.15819","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/limtr-time-series-motion-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2410.15819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15819"}},"official":{"repos":["cing2/limtr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ltboost-boosted-hybrids-of-ensemble-linear","slug":"ltboost-boosted-hybrids-of-ensemble-linear","title":"LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting","date":"2024-10-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-contrastive-learning-for-time-series","slug":"dynamic-contrastive-learning-for-time-series","title":"Dynamic Contrastive Learning for Time Series Representation","date":"2024-10-20","arxiv_id":"2410.15416","repositories_listed":1,"syntology":null},{"url":"/paper/indmask-inductive-explanation-for","slug":"indmask-inductive-explanation-for","title":"IndMask: Inductive Explanation for Multivariate Time Series Black-Box Models","date":"2024-10-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-garch-model-with-two-volatility-components","slug":"a-garch-model-with-two-volatility-components","title":"A GARCH model with two volatility components and two driving factors","date":"2024-10-18","arxiv_id":"2410.14585","repositories_listed":1,"syntology":null},{"url":"/paper/ant-adaptive-noise-schedule-for-time-series","slug":"ant-adaptive-noise-schedule-for-time-series","title":"ANT: Adaptive Noise Schedule for Time Series Diffusion Models","date":"2024-10-18","arxiv_id":"2410.14488","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":11,"n_instrument":4,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":15,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ant-adaptive-noise-schedule-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.14488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14488"}},"official":{"repos":["seunghan96/ant"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hound-high-order-universal-numerical","slug":"hound-high-order-universal-numerical","title":"HOUND: High-Order Universal Numerical Differentiator for a Parameter-free Polynomial Online Approximation","date":"2024-10-18","arxiv_id":"2411.00794","repositories_listed":1,"syntology":null},{"url":"/paper/timeseriesexam-a-time-series-understanding","slug":"timeseriesexam-a-time-series-understanding","title":"TimeSeriesExam: A time series understanding exam","date":"2024-10-18","arxiv_id":"2410.14752","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/timeseriesexam-a-time-series-understanding#ran","syntology_url":"https://syntology.ai/paper/2410.14752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14752"}},"official":null}},{"url":"/paper/unimts-unified-pre-training-for-motion-time","slug":"unimts-unified-pre-training-for-motion-time","title":"UniMTS: Unified Pre-training for Motion Time Series","date":"2024-10-18","arxiv_id":"2410.19818","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unimts-unified-pre-training-for-motion-time#ran","syntology_url":"https://syntology.ai/paper/2410.19818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.19818"}},"official":{"repos":["xiyuanzh/unimts"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/analyzing-deep-transformer-models-for-time","slug":"analyzing-deep-transformer-models-for-time","title":"Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning","date":"2024-10-17","arxiv_id":"2410.13792","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/analyzing-deep-transformer-models-for-time#ran","syntology_url":"https://syntology.ai/paper/2410.13792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13792"}},"official":{"repos":["azencot-group/gatlm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fdf-flexible-decoupled-framework-for-time","slug":"fdf-flexible-decoupled-framework-for-time","title":"FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling","date":"2024-10-17","arxiv_id":"2410.13253","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fdf-flexible-decoupled-framework-for-time#ran","syntology_url":"https://syntology.ai/paper/2410.13253","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13253"}},"official":{"repos":["zjt-gpu/fdf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-flows-for-unveiling-systemic","slug":"graph-neural-flows-for-unveiling-systemic","title":"Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series","date":"2024-10-17","arxiv_id":"2410.14030","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-neural-flows-for-unveiling-systemic#ran","syntology_url":"https://syntology.ai/paper/2410.14030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14030"}},"official":{"repos":["gmerca/gneuralflow"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hiformer-hybrid-frequency-feature-enhancement","slug":"hiformer-hybrid-frequency-feature-enhancement","title":"Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction","date":"2024-10-17","arxiv_id":"2410.13303","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-goodness-of-fit-test-for","slug":"recurrent-neural-goodness-of-fit-test-for","title":"Recurrent Neural Goodness-of-Fit Test for Time Series","date":"2024-10-17","arxiv_id":"2410.13986","repositories_listed":1,"syntology":null},{"url":"/paper/abnormality-forecasting-time-series-anomaly","slug":"abnormality-forecasting-time-series-anomaly","title":"Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling","date":"2024-10-16","arxiv_id":"2410.12206","repositories_listed":1,"syntology":null},{"url":"/paper/catch-channel-aware-multivariate-time-series","slug":"catch-channel-aware-multivariate-time-series","title":"CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching","date":"2024-10-16","arxiv_id":"2410.12261","repositories_listed":1,"syntology":{"n":19,"n_ran":11,"n_constructed":8,"n_ran_checked":7,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":19,"phrase":"11 ran (of which 8 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/catch-channel-aware-multivariate-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.12261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12261"}},"official":{"repos":["decisionintelligence/catch"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":8,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-leitmotifs-in-multidimensional","slug":"discovering-leitmotifs-in-multidimensional","title":"Discovering Leitmotifs in Multidimensional Time Series","date":"2024-10-16","arxiv_id":"2410.12293","repositories_listed":1,"syntology":null},{"url":"/paper/irregularity-informed-time-series-analysis","slug":"irregularity-informed-time-series-analysis","title":"Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics","date":"2024-10-16","arxiv_id":"2410.12257","repositories_listed":1,"syntology":null},{"url":"/paper/towards-neural-scaling-laws-for-time-series","slug":"towards-neural-scaling-laws-for-time-series","title":"Towards Neural Scaling Laws for Time Series Foundation Models","date":"2024-10-16","arxiv_id":"2410.12360","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/towards-neural-scaling-laws-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.12360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12360"}},"official":{"repos":["Qingrenn/TSFM-ScalingLaws"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/llm-mixer-multiscale-mixing-in-llms-for-time","slug":"llm-mixer-multiscale-mixing-in-llms-for-time","title":"LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting","date":"2024-10-15","arxiv_id":"2410.11674","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-foundation-model-for-value-at","slug":"time-series-foundation-model-for-value-at","title":"Time-Series Foundation AI Model for Value-at-Risk Forecasting","date":"2024-10-15","arxiv_id":"2410.11773","repositories_listed":1,"syntology":null},{"url":"/paper/tram-enhancing-user-sleep-prediction-with","slug":"tram-enhancing-user-sleep-prediction-with","title":"TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles","date":"2024-10-15","arxiv_id":"2410.11293","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-probabilistic-ode-solvers-without","slug":"adaptive-probabilistic-ode-solvers-without","title":"Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements","date":"2024-10-14","arxiv_id":"2410.10530","repositories_listed":1,"syntology":null},{"url":"/paper/gift-eval-a-benchmark-for-general-time-series","slug":"gift-eval-a-benchmark-for-general-time-series","title":"GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation","date":"2024-10-14","arxiv_id":"2410.10393","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"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 0 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/gift-eval-a-benchmark-for-general-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.10393","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10393"}},"official":{"repos":["salesforceairesearch/gift-eval"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/moirai-moe-empowering-time-series-foundation","slug":"moirai-moe-empowering-time-series-foundation","title":"Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts","date":"2024-10-14","arxiv_id":"2410.10469","repositories_listed":1,"syntology":null},{"url":"/paper/sensorllm-aligning-large-language-models-with","slug":"sensorllm-aligning-large-language-models-with","title":"SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition","date":"2024-10-14","arxiv_id":"2410.10624","repositories_listed":1,"syntology":null},{"url":"/paper/statiocl-contrastive-learning-for-time-series","slug":"statiocl-contrastive-learning-for-time-series","title":"StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast","date":"2024-10-14","arxiv_id":"2410.10048","repositories_listed":1,"syntology":null},{"url":"/paper/transparent-networks-for-multivariate-time","slug":"transparent-networks-for-multivariate-time","title":"Transparent Networks for Multivariate Time Series","date":"2024-10-14","arxiv_id":"2410.10535","repositories_listed":1,"syntology":null},{"url":"/paper/learning-pattern-specific-experts-for-time","slug":"learning-pattern-specific-experts-for-time","title":"Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift","date":"2024-10-13","arxiv_id":"2410.09836","repositories_listed":1,"syntology":null},{"url":"/paper/mamba4cast-efficient-zero-shot-time-series","slug":"mamba4cast-efficient-zero-shot-time-series","title":"Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models","date":"2024-10-12","arxiv_id":"2410.09385","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mamba4cast-efficient-zero-shot-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.09385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09385"}},"official":{"repos":["automl/mamba4cast"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interdependency-matters-graph-alignment-for","slug":"interdependency-matters-graph-alignment-for","title":"Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection","date":"2024-10-11","arxiv_id":"2410.08877","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/interdependency-matters-graph-alignment-for#ran","syntology_url":"https://syntology.ai/paper/2410.08877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08877"}},"official":{"repos":["wyy-code/MADGA"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/recovering-complex-ecological-dynamics-from","slug":"recovering-complex-ecological-dynamics-from","title":"Recovering complex ecological dynamics from time series using state-space universal dynamic equations","date":"2024-10-11","arxiv_id":"2410.09233","repositories_listed":1,"syntology":null},{"url":"/paper/gla-da-global-local-alignment-domain","slug":"gla-da-global-local-alignment-domain","title":"GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series","date":"2024-10-09","arxiv_id":"2410.06671","repositories_listed":1,"syntology":null},{"url":"/paper/task-oriented-time-series-imputation","slug":"task-oriented-time-series-imputation","title":"Task-oriented Time Series Imputation Evaluation via Generalized Representers","date":"2024-10-09","arxiv_id":"2410.06652","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/task-oriented-time-series-imputation#ran","syntology_url":"https://syntology.ai/paper/2410.06652","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06652"}},"official":{"repos":["hkuedl/Task-Oriented-Imputation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-physics-guided-time-series-embedding","slug":"toward-physics-guided-time-series-embedding","title":"Toward Physics-guided Time Series Embedding","date":"2024-10-09","arxiv_id":"2410.06651","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/toward-physics-guided-time-series-embedding#ran","syntology_url":"https://syntology.ai/paper/2410.06651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.06651"}},"official":null}},{"url":"/paper/towards-generalisable-time-series","slug":"towards-generalisable-time-series","title":"Towards Generalisable Time Series Understanding Across Domains","date":"2024-10-09","arxiv_id":"2410.07299","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/towards-generalisable-time-series#ran","syntology_url":"https://syntology.ai/paper/2410.07299","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07299"}},"official":{"repos":["oetu/otis"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/amortized-control-of-continuous-state-space","slug":"amortized-control-of-continuous-state-space","title":"Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series","date":"2024-10-08","arxiv_id":"2410.05602","repositories_listed":1,"syntology":null},{"url":"/paper/less-is-more-embracing-sparsity-and","slug":"less-is-more-embracing-sparsity-and","title":"Less is more: Embracing sparsity and interpolation with Esiformer for time series forecasting","date":"2024-10-08","arxiv_id":"2410.05726","repositories_listed":1,"syntology":null},{"url":"/paper/vector-icl-in-context-learning-with","slug":"vector-icl-in-context-learning-with","title":"Vector-ICL: In-context Learning with Continuous Vector Representations","date":"2024-10-08","arxiv_id":"2410.05629","repositories_listed":1,"syntology":null},{"url":"/paper/can-llms-understand-time-series-anomalies","slug":"can-llms-understand-time-series-anomalies","title":"Can LLMs Understand Time Series Anomalies?","date":"2024-10-07","arxiv_id":"2410.05440","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/can-llms-understand-time-series-anomalies#ran","syntology_url":"https://syntology.ai/paper/2410.05440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05440"}},"official":{"repos":["rose-stl-lab/anomllm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-fourier-modelling-a-highly-compact","slug":"neural-fourier-modelling-a-highly-compact","title":"Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis","date":"2024-10-07","arxiv_id":"2410.04703","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-fourier-modelling-a-highly-compact#ran","syntology_url":"https://syntology.ai/paper/2410.04703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04703"}},"official":{"repos":["minkiml/NFM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/timebridge-non-stationarity-matters-for-long","slug":"timebridge-non-stationarity-matters-for-long","title":"TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting","date":"2024-10-06","arxiv_id":"2410.04442","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/timebridge-non-stationarity-matters-for-long#ran","syntology_url":"https://syntology.ai/paper/2410.04442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04442"}},"official":{"repos":["hank0626/timebridge"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/autoregressive-moving-average-attention","slug":"autoregressive-moving-average-attention","title":"Autoregressive Moving-average Attention Mechanism for Time Series Forecasting","date":"2024-10-04","arxiv_id":"2410.03159","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"6 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/autoregressive-moving-average-attention#ran","syntology_url":"https://syntology.ai/paper/2410.03159","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03159"}},"official":{"repos":["ljc-fvnr/arma-attention"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gas-norm-score-driven-adaptive-normalization","slug":"gas-norm-score-driven-adaptive-normalization","title":"GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning","date":"2024-10-04","arxiv_id":"2410.03935","repositories_listed":1,"syntology":null},{"url":"/paper/local-attention-mechanism-boosting-the","slug":"local-attention-mechanism-boosting-the","title":"Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting","date":"2024-10-04","arxiv_id":"2410.03805","repositories_listed":1,"syntology":null},{"url":"/paper/sda-grin-for-adaptive-spatial-temporal","slug":"sda-grin-for-adaptive-spatial-temporal","title":"SDA-GRIN for Adaptive Spatial-Temporal Multivariate Time Series Imputation","date":"2024-10-04","arxiv_id":"2410.03954","repositories_listed":1,"syntology":null},{"url":"/paper/backtime-backdoor-attacks-on-multivariate","slug":"backtime-backdoor-attacks-on-multivariate","title":"BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting","date":"2024-10-03","arxiv_id":"2410.02195","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/backtime-backdoor-attacks-on-multivariate#ran","syntology_url":"https://syntology.ai/paper/2410.02195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02195"}},"official":{"repos":["xiaolin-cs/backtime"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/candoit-causal-discovery-with-observational","slug":"candoit-causal-discovery-with-observational","title":"CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series","date":"2024-10-03","arxiv_id":"2410.02844","repositories_listed":1,"syntology":null},{"url":"/paper/channel-aware-contrastive-conditional","slug":"channel-aware-contrastive-conditional","title":"Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting","date":"2024-10-03","arxiv_id":"2410.02168","repositories_listed":1,"syntology":null},{"url":"/paper/mixlinear-extreme-low-resource-multivariate","slug":"mixlinear-extreme-low-resource-multivariate","title":"MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameters","date":"2024-10-02","arxiv_id":"2410.02081","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mixlinear-extreme-low-resource-multivariate#ran","syntology_url":"https://syntology.ai/paper/2410.02081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02081"}},"official":{"repos":["lss-1138/SparseTSF"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mmfnet-multi-scale-frequency-masking-neural","slug":"mmfnet-multi-scale-frequency-masking-neural","title":"MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting","date":"2024-10-02","arxiv_id":"2410.02070","repositories_listed":1,"syntology":null},{"url":"/paper/bayes-catsi-a-variational-bayesian-approach","slug":"bayes-catsi-a-variational-bayesian-approach","title":"Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation","date":"2024-10-01","arxiv_id":"2410.01847","repositories_listed":1,"syntology":null},{"url":"/paper/frequency-adaptive-normalization-for-non","slug":"frequency-adaptive-normalization-for-non","title":"Frequency Adaptive Normalization For Non-stationary Time Series Forecasting","date":"2024-09-30","arxiv_id":"2409.20371","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/frequency-adaptive-normalization-for-non#ran","syntology_url":"https://syntology.ai/paper/2409.20371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.20371"}},"official":{"repos":["wayne155/FAN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/model-selection-with-a-shapelet-based","slug":"model-selection-with-a-shapelet-based","title":"Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification","date":"2024-09-30","arxiv_id":"2409.20005","repositories_listed":1,"syntology":null},{"url":"/paper/stream-level-flow-matching-from-a-bayesian","slug":"stream-level-flow-matching-from-a-bayesian","title":"Stream-level flow matching with Gaussian processes","date":"2024-09-30","arxiv_id":"2409.20423","repositories_listed":1,"syntology":null},{"url":"/paper/tsi-a-multi-view-representation-learning","slug":"tsi-a-multi-view-representation-learning","title":"TSI: A Multi-View Representation Learning Approach for Time Series Forecasting","date":"2024-09-30","arxiv_id":"2409.19871","repositories_listed":1,"syntology":null},{"url":"/paper/evolving-multi-scale-normalization-for-time","slug":"evolving-multi-scale-normalization-for-time","title":"Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts","date":"2024-09-29","arxiv_id":"2409.19718","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-source-recovery-for-time-series","slug":"temporal-source-recovery-for-time-series","title":"Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation","date":"2024-09-29","arxiv_id":"2409.19635","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-time-series-forecasting-a","slug":"optimizing-time-series-forecasting-a","title":"Optimizing Time Series Forecasting: A Comparative Study of Adam and Nesterov Accelerated Gradient on LSTM and GRU networks Using Stock Market data","date":"2024-09-28","arxiv_id":"2410.01843","repositories_listed":1,"syntology":null},{"url":"/paper/cesnet-timeseries24-time-series-dataset-for","slug":"cesnet-timeseries24-time-series-dataset-for","title":"CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting","date":"2024-09-27","arxiv_id":"2409.18874","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cesnet-timeseries24-time-series-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2409.18874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18874"}},"official":{"repos":["koumajos/CESNET-TimeSeries24-Example"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cyclenet-enhancing-time-series-forecasting","slug":"cyclenet-enhancing-time-series-forecasting","title":"CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns","date":"2024-09-27","arxiv_id":"2409.18479","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cyclenet-enhancing-time-series-forecasting#ran","syntology_url":"https://syntology.ai/paper/2409.18479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18479"}},"official":{"repos":["ACAT-SCUT/CycleNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-the-power-of-timestamps-for-robust","slug":"rethinking-the-power-of-timestamps-for-robust","title":"Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective","date":"2024-09-27","arxiv_id":"2409.18696","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-the-power-of-timestamps-for-robust#ran","syntology_url":"https://syntology.ai/paper/2409.18696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18696"}},"official":{"repos":["ForestsKing/GLAFF"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-time-series-is-worth-five-experts-1","slug":"a-time-series-is-worth-five-experts-1","title":"A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction","date":"2024-09-26","arxiv_id":"2409.17440","repositories_listed":1,"syntology":null},{"url":"/paper/from-news-to-forecast-integrating-event","slug":"from-news-to-forecast-integrating-event","title":"From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection","date":"2024-09-26","arxiv_id":"2409.17515","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/from-news-to-forecast-integrating-event#ran","syntology_url":"https://syntology.ai/paper/2409.17515","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17515"}},"official":{"repos":["ameliawong1996/From_News_to_Forecast"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/mamba-meets-financial-markets-a-graph-mamba","slug":"mamba-meets-financial-markets-a-graph-mamba","title":"Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction","date":"2024-09-26","arxiv_id":"2410.03707","repositories_listed":1,"syntology":null},{"url":"/paper/pgn-the-rnn-s-new-successor-is-effective-for","slug":"pgn-the-rnn-s-new-successor-is-effective-for","title":"PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting","date":"2024-09-26","arxiv_id":"2409.17703","repositories_listed":1,"syntology":null},{"url":"/paper/the-elephant-in-the-room-towards-a-reliable","slug":"the-elephant-in-the-room-towards-a-reliable","title":"The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark","date":"2024-09-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/using-dynamic-loss-weighting-to-boost","slug":"using-dynamic-loss-weighting-to-boost","title":"Using dynamic loss weighting to boost improvements in forecast stability","date":"2024-09-26","arxiv_id":"2409.18267","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-image-database-a-dataset-of-ecg-images","slug":"ecg-image-database-a-dataset-of-ecg-images","title":"ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis","date":"2024-09-25","arxiv_id":"2409.16612","repositories_listed":1,"syntology":null},{"url":"/paper/emit-event-based-masked-auto-encoding-for","slug":"emit-event-based-masked-auto-encoding-for","title":"EMIT- Event-Based Masked Auto Encoding for Irregular Time Series","date":"2024-09-25","arxiv_id":"2409.16554","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/emit-event-based-masked-auto-encoding-for#ran","syntology_url":"https://syntology.ai/paper/2409.16554","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.16554"}},"official":{"repos":["hrishi-ds/EMIT"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/optimal-starting-point-for-time-series","slug":"optimal-starting-point-for-time-series","title":"Optimal starting point for time series forecasting","date":"2024-09-25","arxiv_id":"2409.16843","repositories_listed":1,"syntology":null},{"url":"/paper/time-moe-billion-scale-time-series-foundation","slug":"time-moe-billion-scale-time-series-foundation","title":"Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts","date":"2024-09-24","arxiv_id":"2409.16040","repositories_listed":1,"syntology":{"n":20,"n_ran":14,"n_constructed":9,"n_ran_checked":11,"n_instrument":3,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"14 ran (of which 9 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/time-moe-billion-scale-time-series-foundation#ran","syntology_url":"https://syntology.ai/paper/2409.16040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.16040"}},"official":{"repos":["time-moe/time-moe"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":9,"n_ran_no_instrument_failure":11,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/tsfeatlime-an-online-user-study-in-enhancing","slug":"tsfeatlime-an-online-user-study-in-enhancing","title":"TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting","date":"2024-09-24","arxiv_id":"2409.15950","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-forecasting-of-chaotic-systems","slug":"zero-shot-forecasting-of-chaotic-systems","title":"Zero-shot forecasting of chaotic systems","date":"2024-09-24","arxiv_id":"2409.15771","repositories_listed":1,"syntology":{"n":20,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/zero-shot-forecasting-of-chaotic-systems#ran","syntology_url":"https://syntology.ai/paper/2409.15771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.15771"}},"official":{"repos":["williamgilpin/dysts"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/identifying-elasticities-in-autocorrelated","slug":"identifying-elasticities-in-autocorrelated","title":"Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs","date":"2024-09-23","arxiv_id":"2409.15530","repositories_listed":1,"syntology":null}],"record_sha256":"cdd4be65a647a96151640aafd454284ed3f57e36e445ccb91ecee7e12f57d3a7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}