{"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-forecasting/papers/8","list_of":"/task/time-series-forecasting","task":"Time Series Forecasting","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":8,"pages_in_order":17,"rows_per_page":100,"rows":[701,800],"of":1609,"counts":{"archive_papers_tagged":1609,"with_a_code_link":714,"where_syntology_ran_a_sample":211,"not_listed_spam_title":0,"listed":1609,"listed_where_code_ran":211,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":186,"every_run_a_failure_of_syntologys_instrument":25,"listed_with_a_run_with_no_instrument_failure":186,"listed_every_run_a_failure_of_syntologys_instrument":25,"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-forecasting","prev":"/task/time-series-forecasting/papers/7","next":"/task/time-series-forecasting/papers/9","papers":[{"url":"/paper/streaming-adaptation-of-deep-forecasting","slug":"streaming-adaptation-of-deep-forecasting","title":"Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units","date":"2019-06-24","arxiv_id":"1906.09926","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-time-series-forecasting-models-an","slug":"evaluating-time-series-forecasting-models-an","title":"Evaluating time series forecasting models: An empirical study on performance estimation methods","date":"2019-05-28","arxiv_id":"1905.11744","repositories_listed":1,"syntology":null},{"url":"/paper/stg2seq-spatial-temporal-graph-to-sequence","slug":"stg2seq-spatial-temporal-graph-to-sequence","title":"STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting","date":"2019-05-24","arxiv_id":"1905.10069","repositories_listed":1,"syntology":null},{"url":"/paper/think-globally-act-locally-a-deep-neural","slug":"think-globally-act-locally-a-deep-neural","title":"Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting","date":"2019-05-09","arxiv_id":"1905.03806","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/think-globally-act-locally-a-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1905.03806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.03806"}},"official":{"repos":["rajatsen91/deepglo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/temporal-attention-augmented-bilinear-network-1","slug":"temporal-attention-augmented-bilinear-network-1","title":"Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis","date":"2019-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-forecasting-of-sensory-data","slug":"probabilistic-forecasting-of-sensory-data","title":"Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN","date":"2019-03-29","arxiv_id":"1903.12549","repositories_listed":1,"syntology":null},{"url":"/paper/st-lstm-a-deep-learning-approach-combined","slug":"st-lstm-a-deep-learning-approach-combined","title":"ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term","date":"2019-01-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-line-learning-of-linear-dynamical-systems","slug":"on-line-learning-of-linear-dynamical-systems","title":"On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters","date":"2018-09-16","arxiv_id":"1809.05870","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/on-line-learning-of-linear-dynamical-systems#ran","syntology_url":"https://syntology.ai/paper/1809.05870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05870"}},"official":null}},{"url":"/paper/a-capsule-network-for-traffic-speed","slug":"a-capsule-network-for-traffic-speed","title":"A Capsule Network for Traffic Speed Prediction in Complex Road Networks","date":"2018-07-23","arxiv_id":"1807.10603","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-uncertainties-for-deep-learning","slug":"accurate-uncertainties-for-deep-learning","title":"Accurate Uncertainties for Deep Learning Using Calibrated Regression","date":"2018-07-01","arxiv_id":"1807.00263","repositories_listed":1,"syntology":null},{"url":"/paper/mordred-memory-based-ordinal-regression-deep","slug":"mordred-memory-based-ordinal-regression-deep","title":"MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting","date":"2018-03-26","arxiv_id":"1803.09704","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multi-view-spatial-temporal-network-for","slug":"deep-multi-view-spatial-temporal-network-for","title":"Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction","date":"2018-02-23","arxiv_id":"1802.08714","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/deep-multi-view-spatial-temporal-network-for#ran","syntology_url":"https://syntology.ai/paper/1802.08714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08714"}},"official":{"repos":["huaxiuyao/DMVST-Net"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/dilated-convolutional-neural-networks-for-3","slug":"dilated-convolutional-neural-networks-for-3","title":"Dilated Convolutional Neural Networks for Time Series Forecasting","date":"2018-01-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/temporal-attention-augmented-bilinear-network","slug":"temporal-attention-augmented-bilinear-network","title":"Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis","date":"2017-12-04","arxiv_id":"1712.00975","repositories_listed":1,"syntology":null},{"url":null,"slug":"the-power-of-architecture-deep-dive-into","title":"The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting","date":"2025-07-17","arxiv_id":"2507.13043","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-in-time-series-forecasting","title":"Data Augmentation in Time Series Forecasting through Inverted Framework","date":"2025-07-15","arxiv_id":"2507.11439","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-last-mile-of-prediction","title":"Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching","date":"2025-07-09","arxiv_id":"2507.07192","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-models-for-time-series-forecasting","title":"Foundation models for time series forecasting: Application in conformal prediction","date":"2025-07-09","arxiv_id":"2507.08858","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-based-demand-forecasting-and-load","title":"AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study","date":"2025-07-08","arxiv_id":"2507.06077","repositories_listed":0,"syntology":null},{"url":null,"slug":"seed-a-structural-encoder-for-embedding","title":"SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs","date":"2025-06-25","arxiv_id":"2506.20167","repositories_listed":0,"syntology":null},{"url":null,"slug":"faf-a-feature-adaptive-framework-for-few-shot","title":"FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting","date":"2025-06-24","arxiv_id":"2506.19567","repositories_listed":0,"syntology":null},{"url":null,"slug":"flightkooba-a-fast-interpretable-ftp-model","title":"FlightKooba: A Fast Interpretable FTP Model","date":"2025-06-24","arxiv_id":"2506.19885","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-machine-learning-algorithms-using","title":"Scalable Machine Learning Algorithms using Path Signatures","date":"2025-06-21","arxiv_id":"2506.17634","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-forecasting-accuracy-in-dynamic","title":"Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation","date":"2025-06-17","arxiv_id":"2506.14133","repositories_listed":0,"syntology":null},{"url":"/paper/multi-scale-finetuning-for-encoder-based-time","slug":"multi-scale-finetuning-for-encoder-based-time","title":"Multi-Scale Finetuning for Encoder-based Time Series Foundation Models","date":"2025-06-17","arxiv_id":"2506.14087","repositories_listed":0,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":13,"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) · 6 unverified","sample_list":"/paper/multi-scale-finetuning-for-encoder-based-time#ran","syntology_url":"https://syntology.ai/paper/2506.14087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14087"}},"official":null}},{"url":null,"slug":"skolr-structured-koopman-operator-linear-rnn","title":"SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting","date":"2025-06-17","arxiv_id":"2506.14113","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecast-then-optimize-deep-learning-methods","title":"Forecast-Then-Optimize Deep Learning Methods","date":"2025-06-16","arxiv_id":"2506.13036","repositories_listed":0,"syntology":null},{"url":null,"slug":"metaeformer-unveiling-and-leveraging-meta","title":"MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting","date":"2025-06-15","arxiv_id":"2506.12800","repositories_listed":0,"syntology":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":"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":"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":"uncovering-the-functional-roles-of","title":"Uncovering the Functional Roles of Nonlinearity in Memory","date":"2025-06-09","arxiv_id":"2506.07919","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-probabilistic-framework-for","title":"End-to-End Probabilistic Framework for Learning with Hard Constraints","date":"2025-06-08","arxiv_id":"2506.07003","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-framework-for-model-selection","title":"A Statistical Framework for Model Selection in LSTM Networks","date":"2025-06-07","arxiv_id":"2506.06840","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-04677","title":"The cost of ensembling: is it always worth combining?","date":"2025-06-05","arxiv_id":"2506.04677","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":"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":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/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":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":"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":"comparative-analysis-of-financial-data","title":"Comparative analysis of financial data differentiation techniques using LSTM neural network","date":"2025-05-25","arxiv_id":"2505.19243","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":"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":"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":"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":"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":"rifles-resource-efficient-federated-learning","title":"RIFLES: Resource-effIcient Federated LEarning via Scheduling","date":"2025-05-19","arxiv_id":"2505.13169","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":"stochastic-processes-with-modified-lognormal","title":"Stochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail","date":"2025-05-17","arxiv_id":"2505.14713","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-forecasting-mortality-rates-a","title":"Zero-Shot Forecasting Mortality Rates: A Global Study","date":"2025-05-17","arxiv_id":"2505.13521","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":"2505-11250","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":"2505-11349","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":"2505-11390","title":"IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting","date":"2025-05-16","arxiv_id":"2505.11390","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":"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":"avocado-price-prediction-using-a-hybrid-deep","title":"Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture","date":"2025-05-15","arxiv_id":"2505.09907","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":"fas-llm-large-language-model-based-channel","title":"FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links","date":"2025-05-14","arxiv_id":"2505.09751","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":"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":"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":"matrix-is-all-you-need","title":"Matrix Is All You Need","date":"2025-05-11","arxiv_id":"2506.01966","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-efficient-multivariate-time","title":"Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering","date":"2025-05-09","arxiv_id":"2505.05738","repositories_listed":0,"syntology":null},{"url":null,"slug":"strgcn-capturing-asynchronous-spatio-temporal","title":"STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting","date":"2025-05-07","arxiv_id":"2505.04167","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-is-more-efficient-weight-farcasting-with","title":"Less is More: Efficient Weight Farcasting with 1-Layer Neural Network","date":"2025-05-05","arxiv_id":"2505.02714","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-simplest-neural-ode","title":"Learning the Simplest Neural ODE","date":"2025-05-04","arxiv_id":"2505.02019","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-prediction-model-for-time-series","title":"Enhanced Prediction Model for Time Series Characterized by GARCH via Interval Type-2 Fuzzy Inference System","date":"2025-05-03","arxiv_id":"2505.01675","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-forecaster-a-multimodal-time-series","title":"Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts","date":"2025-05-02","arxiv_id":"2505.01135","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-optimization-for-time-series","title":"Feature Optimization for Time Series Forecasting via Novel Randomized Uphill Climbing","date":"2025-05-02","arxiv_id":"2505.03805","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-attention-evolutional-graph","title":"Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting","date":"2025-05-01","arxiv_id":"2505.00302","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-the-potential-of-linear-networks","title":"Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting","date":"2025-05-01","arxiv_id":"2505.00590","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-time-series-forecasting-of","title":"Probabilistic Time Series Forecasting of Residential Loads -- A Copula Approach","date":"2025-04-30","arxiv_id":"2504.21661","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-conditioned-diffusive-time-series","title":"Multimodal Conditioned Diffusive Time Series Forecasting","date":"2025-04-28","arxiv_id":"2504.19669","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-short-and-long-term-dependencies-a","title":"Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting","date":"2025-04-27","arxiv_id":"2504.19309","repositories_listed":0,"syntology":null},{"url":null,"slug":"goal-oriented-time-series-forecasting","title":"Goal-Oriented Time-Series Forecasting: Foundation Framework Design","date":"2025-04-24","arxiv_id":"2504.17493","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-hybrid-approach-using-an-attention","title":"A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices","date":"2025-04-23","arxiv_id":"2504.17079","repositories_listed":0,"syntology":null},{"url":null,"slug":"itfkan-interpretable-time-series-forecasting","title":"iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network","date":"2025-04-23","arxiv_id":"2504.16432","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-model-learning-with-data-assimilated","title":"Online model learning with data-assimilated reservoir computers","date":"2025-04-23","arxiv_id":"2504.16767","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-temporal-plasticity-in-foundation","title":"Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning","date":"2025-04-20","arxiv_id":"2504.14677","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-local-representation-alignment-rnns-solve","title":"Can Local Representation Alignment RNNs Solve Temporal Tasks?","date":"2025-04-18","arxiv_id":"2504.13531","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmformer-with-adaptive-transferable-attention","title":"MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications","date":"2025-04-18","arxiv_id":"2504.14050","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-competition-enhance-the-proficiency-of","title":"Can Competition Enhance the Proficiency of Agents Powered by Large Language Models in the Realm of News-driven Time Series Forecasting?","date":"2025-04-14","arxiv_id":"2504.10210","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-to-the-unknown-robust-meta-learning","title":"Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting","date":"2025-04-13","arxiv_id":"2504.09664","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-mamba-multi-scale-mamba-for-time-series","title":"ms-Mamba: Multi-scale Mamba for Time-Series Forecasting","date":"2025-04-10","arxiv_id":"2504.07654","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-qos-metric-forecasting-in-delay","title":"Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics","date":"2025-04-09","arxiv_id":"2504.08821","repositories_listed":0,"syntology":null},{"url":null,"slug":"loss-functions-in-deep-learning-a","title":"Loss Functions in Deep Learning: A Comprehensive Review","date":"2025-04-05","arxiv_id":"2504.04242","repositories_listed":0,"syntology":null},{"url":null,"slug":"block-toeplitz-sparse-precision-matrix","title":"Block Toeplitz Sparse Precision Matrix Estimation for Large-Scale Interval-Valued Time Series Forecasting","date":"2025-04-04","arxiv_id":"2504.03322","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-model-selection-for-time-series","title":"Efficient Model Selection for Time Series Forecasting via LLMs","date":"2025-04-02","arxiv_id":"2504.02119","repositories_listed":0,"syntology":null}],"record_sha256":"742a10d8b83b7cb3d1ff6eca55616e206509840b8e7bc4d32021e10e22a2b255","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}