{"total":46,"rows":[{"id":"2403.13724","title":"Probabilistic Forecasting with Stochastic Interpolants and F\\\"ollmer Processes","created":"2024-03-20","cited_by_count":66,"area":null},{"id":"2210.03675","title":"Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts","created":"2022-10-07","cited_by_count":37,"area":null},{"id":"2312.17346","title":"STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction","created":"2023-12-28","cited_by_count":35,"area":null},{"id":"2202.01712","title":"Review of automated time series forecasting pipelines","created":"2022-02-03","cited_by_count":18,"area":null},{"id":"2208.13251","title":"A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms","created":"2022-08-28","cited_by_count":7,"area":null},{"id":"2306.06252","title":"Feature Programming for Multivariate Time Series Prediction","created":"2023-06-09","cited_by_count":7,"area":null},{"id":"2210.04797","title":"DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions","created":"2022-09-23","cited_by_count":6,"area":null},{"id":"2103.00092","title":"The Age of Correlated Features in Supervised Learning based Forecasting","created":"2021-02-27","cited_by_count":5,"area":null},{"id":"2305.08890","title":"Differential Convolutional Fuzzy Time Series Forecasting","created":"2023-05-15","cited_by_count":5,"area":null},{"id":"2506.08274","title":"The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks","created":"2025-06-09","cited_by_count":4,"area":null},{"id":"2209.10705","title":"Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators","created":"2022-09-21","cited_by_count":3,"area":null},{"id":"2308.00709","title":"DeepTSF: Codeless machine learning operations for time series forecasting","created":"2023-07-28","cited_by_count":3,"area":null},{"id":"2410.18268","title":"Stabilizing black-box model selection with the inflated argmax","created":"2024-10-23","cited_by_count":3,"area":null},{"id":"2412.20510","title":"Stratify: Unifying Multi-Step Forecasting Strategies","created":"2024-12-29","cited_by_count":3,"area":null},{"id":"2302.04018","title":"A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas","created":"2023-02-08","cited_by_count":2,"area":null},{"id":"2307.06645","title":"Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2405.06089","title":"Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2411.10009","title":"Semiparametric inference for impulse response functions using double/debiased machine learning","created":"2024-11-15","cited_by_count":2,"area":null},{"id":"2603.20980","title":"From Causal Discovery to Dynamic Causal Inference in Neural Time Series","created":"2026-03-21","cited_by_count":2,"area":null},{"id":"2207.14219","title":"A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting","created":"2022-07-28","cited_by_count":1,"area":null},{"id":"2212.07771","title":"Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting","created":"2022-12-15","cited_by_count":1,"area":null},{"id":"2307.12395","title":"Concentration inequalities for high-dimensional linear processes with dependent innovations","created":"2023-07-23","cited_by_count":1,"area":null},{"id":"2308.00855","title":"A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades","created":"2023-08-01","cited_by_count":1,"area":null},{"id":"2308.10993","title":"Econometrics of Machine Learning Methods in Economic Forecasting","created":"2023-08-21","cited_by_count":1,"area":null},{"id":"2312.08528","title":"auto-sktime: Automated Time Series Forecasting","created":"2023-12-13","cited_by_count":1,"area":null},{"id":"2404.08712","title":"Machine learning and economic forecasting: the role of international trade networks","created":"2024-04-11","cited_by_count":1,"area":null},{"id":"2410.17587","title":"Predicting Company Growth using Scaling Theory informed Machine Learning","created":"2024-10-23","cited_by_count":1,"area":null},{"id":"2411.17499","title":"Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2502.09981","title":"Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data","created":"2025-02-14","cited_by_count":1,"area":null},{"id":"2503.06928","title":"FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2507.21357","title":"A Contrastive Diffusion-based Network (CDNet) for Time Series Classification","created":"2025-07-28","cited_by_count":1,"area":null},{"id":"2110.03146","title":"Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning","created":"2021-10-07","cited_by_count":0,"area":null},{"id":"2207.05708","title":"Improved Batching Strategy For Irregular Time-Series ODE","created":"2022-07-12","cited_by_count":0,"area":null},{"id":"2209.08618","title":"Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data","created":"2022-09-18","cited_by_count":0,"area":null},{"id":"2211.08268","title":"A Comparative Study of Machine Learning and Deep Learning Techniques for Prediction of Co2 Emission in Cars","created":"2022-11-15","cited_by_count":0,"area":null},{"id":"2307.06970","title":"Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens","created":"2023-07-13","cited_by_count":0,"area":null},{"id":"2307.14294","title":"Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis","created":"2023-07-26","cited_by_count":0,"area":null},{"id":"2402.06638","title":"Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2405.06986","title":"Revisiting the Efficacy of Signal Decomposition in AI-based Time Series Prediction","created":"2024-05-11","cited_by_count":0,"area":null},{"id":"2502.09947","title":"Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model","created":"2025-02-14","cited_by_count":0,"area":null},{"id":"2503.17214","title":"ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2506.07920","title":"W4S4: WaLRUS Meets S4 for Long-Range Sequence Modeling","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2509.17063","title":"TSGym: Design Choices for Deep Multivariate Time-Series Forecasting","created":"2025-09-21","cited_by_count":0,"area":null},{"id":"2510.17106","title":"Fighter: Unveiling the Graph Convolutional Nature of Transformers in Time Series Modeling","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2605.26562","title":"Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.15213","title":"Quantum-classical hybrid models based on error correction for time series forecasting","created":"2026-06-13","cited_by_count":0,"area":null}],"next_cursor":null}