「forecasting」の検索結果
35 件Standard spatiotemporal graph convolutional networks need a fixed-size adjacency matrix, coupling model weight
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural esti
Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal cont
Transolver has recently gained widespread adoption for solving diverse physical problems on unstructured geome
Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed ris
Graph forecasting systems often rely on prediction intervals to quantify uncertainty across connected location
Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains uncl
Time series foundation models (TSFMs) are increasingly used for probabilistic forecasting across diverse domai
Graph forecasters for sensor networks are trained in one regime and deployed in others, where sensor coupling
Hybrid models and neural differential equations (NDEs) are becoming increasingly important for modelling physi
Spherical-harmonic neural operators provide an efficient representation for global forecasting, but their disc
Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large
Responses with headers, lists and greater length receive high scores from reward models (RMs), and RLHF polici
Large language model agents increasingly take consequential actions in digital and physical environments, wher
Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant eviden
Automatic rigging is a fundamental task for both static 3D and dynamic 4D mesh animation, yet existing methods
Estimating the metric 3D position of people from a monocular camera is important for robot navigation and moti
3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous dr
Accurate forecasting and temporal downscaling of solar irradiance is critical for reliable renewable energy in
Scaling laws predict the outcome of large, expensive training runs by extrapolating trends from smaller, cheap
Anticipating financial outcomes requires synthesizing evolving evidence and reasoning under uncertainty, makin
Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its f
Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications rangi
As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden o
Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and ope
Streaming federated learning (SFL) allows clients to train a shared model from continuously arriving data with
Time-series forecasters are ranked almost entirely by accuracy metrics, but a forecast's decision value, its e
Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions ev
Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line it
Accurate forecasting of global total electron content (TEC) is important for space weather monitoring and the
Nonlinear structural system identification remains challenging when governing physics is only partially known
Traffic forecasting requires modeling complex temporal dynamics and the spatial dependencies imposed by road t
Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when
Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trai
Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolati