www.bing.com/ck/a?!&&p=071f4a057874a8a3dfb30015445a26b27942680a25d2d6ed290e9173c242f316JmltdHM9MTc3Mjg0MTYwMA&ptn=3&ver=2&hsh=4&fclid=39544dda-6ef5-6c85-09ac-5acf6f546d7a&u=a1aHR0cHM6Ly93d3cueW91cndlYXRoZXIuY28udWsvd2VhdGhlci1tYXBzL3RlbXAybS11a24uaHRtbA&ntb=1
Weather map with the Temperature forecast for the United Kingdom. Temperature forecast for the next 14 days. Forecast for the United Kingdom.
arxiv.org/abs/1805.09902v4
Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical conditions...
arxiv.org/abs/2105.05993v1
The EU funded the FLARECAST project, that ran from Jan 2015 until Feb 2018. FLARECAST had a R2O focus, and introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of...
arxiv.org/abs/2306.08299v1
Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of t...
www.bing.com/ck/a?!&&p=008658e614a5c345ebb575c59ec04a95956e8842a12c2963eaa1fef2bf103f66JmltdHM9MTc3Mjc1NTIwMA&ptn=3&ver=2&hsh=4&fclid=055ef072-86a4-634d-25a0-e76787e56261&u=a1aHR0cHM6Ly93d3cubXlmb3JlY2FzdC5jb20vMTUtZGF5LWZvcmVjYXN0LnBocD9jd2lkPTIyMTcx&ntb=1
Feb 10, 2026 · Springfield, Missouri | 03:50 AM 15 Day Forecast Rise: 7:07AM | Set: 5:47PM 10hrs 40mins Rise: 2:04AM | Set: 11:29AM Last Quarter
arxiv.org/abs/2112.03108v2
It is important to forecast dam inflow for flood damage mitigation. The hydrograph provides critical information such as the start time, peak level, and volume. Particularly, dam management requires a 6-h lead time of the dam inflow forecast based on...
arxiv.org/abs/2510.15821v1
Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real...
arxiv.org/abs/2005.06978v1
Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced products insufficient training data is available to train accurate models. I...
arxiv.org/abs/2112.06266v1
The R package knnwtsim provides functions to implement k nearest neighbors (KNN) forecasting using a similarity metric tailored to the forecasting problem of predicting future observations of a response series where recent observations, seasonal or p...
arxiv.org/abs/2410.11674v2
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series d...
arxiv.org/abs/2312.03528v1
Human pose forecasting is the task of predicting articulated human motion given past human motion. There exists a number of popular benchmarks that evaluate an array of different models performing human pose forecasting. These benchmarks do not refle...
arxiv.org/abs/2211.08661v1
Threshold Autoregressive (TAR) models have been widely used by statisticians for non-linear time series forecasting during the past few decades, due to their simplicity and mathematical properties. On the other hand, in the forecasting community, gen...
arxiv.org/abs/2203.00937v1
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting model combining exponential smooth...
arxiv.org/abs/1610.05183v1
We present a model for generating probabilistic forecasts by combining kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. The KDE...
www.bing.com/ck/a?!&&p=52c6fd334aaef863cc69c10db18e7378cbe96a2f45b0c112251c7d97020cfa2cJmltdHM9MTc3MjU4MjQwMA&ptn=3&ver=2&hsh=4&fclid=29e43b36-fbad-6588-2e95-2c24faf86483&u=a1aHR0cHM6Ly93d3cud2VhdGhlci1mb3JlY2FzdC5jb20vbG9jYXRpb25zL0F0aGVucy9mb3JlY2FzdHMvbGF0ZXN0&ntb=1
1 day ago · Athens Weather Forecast Athens (Attica, Greece) forecast issued: 7 am Tue 03 Mar 2026 local time | Updates in: 5hr 17 min 45 s Greece Athens
arxiv.org/abs/1711.02247v2
In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-dr...
arxiv.org/abs/2312.15235v1
Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Exist...
arxiv.org/abs/2510.04386v1
Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We present SSM-CGM, a Mamba-based neural state-space forecasting model that in...
arxiv.org/abs/1212.6678v1
This document is part of a series of near real-time weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of a forecast initiated following assimilation of observations for Week 51 (i.e. the forecast begins De...
arxiv.org/abs/1212.4678v1
We present results of a forecast initiated Week 49 (beginning December 9, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 14, 2012. Results from forecasts initiated the two previous w...
