CNN improves medium-range temperature forecasts with limited resources.
problem Limited computational resources for high-resolution temperature forecasts.
method CNN post-processing with ensemble NWP models for bias correction and spatial downscaling.
result High-resolution (5-km) surface temperature forecasts with lead times up to 5.5 days.
The paper analyzes extreme temperature forecasting using machine learning models.
problem Forecasting extreme temperatures in U.S. cities.
method Auto-Regressive Integrated Moving Average, Exponential Smoothing, Multilayer Perceptrons, Gaussian Processes.
result Multilayer Perceptrons were found to be the most effective approach for forecasting extreme temperatures.
Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neigh…
ABC method improves subseasonal weather forecasting by 60-90%.
problem Improving subseasonal temperature and precipitation forecasting accuracy.
method Combines dynamical forecasts with machine learning-based bias correction.
result Significant improvement in temperature and precipitation forecasting skills.
Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
DYffusion improves diffusion models for spatiotemporal forecasting.
problem Challenges in generating stable and accurate forecasts for dynamic data.
method Leverages temporal dynamics in data, directly coupling it with diffusion steps.
result Improves computational efficiency and performs competitively on complex dynamics.
ARX models predict thermal behavior of WBG semiconductors accurately.
problem Thermal management challenges of WBG semiconductors.
method Use of ARX parametric models based on experimental measurements.
result ARX models provide accurate temperature predictions without detailed component information.
Machine learning speeds up CRTM model predictions for weather forecasting.
problem Insufficient computational efficiency of radiative transfer models.
method Probabilistic neural network emulator of CRTM.
result Emulator predicts brightness temperatures with RMSE < 0.1 K for clear sky conditions.
Deep learning model predicts European weather parameters.
problem Ensemble weather prediction using deep learning.
method Conditional deep convolutional generative adversarial network (GAN) and Monte-Carlo dropout.
result Forecast skill for geopotential height and two-meter temperature is good, but precipitation is challenging.
TSCoNet forecasts correlated geophysical fields with uncertainty estimates.
problem Accurate and reliable forecasts of correlated geophysical fields across many locations.
method Two-stage CNN-LSTM coupled with Gaussian copula.
result Calibrated prediction intervals without sacrificing point accuracy.
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…
Kernel methods improve geophysical forecasting accuracy and efficiency.
problem Improving geophysical forecasting models for accuracy and efficiency.
method Data-driven modeling of geophysical processes using kernel flows for faster, more accurate predictions.
result Kernel methods outperform neural networks and classical models in geophysical forecasting.
Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior probability of the enti…
We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting ele…
The paper uses neural networks to forecast time series data.
problem Forecasting high-dimensional stationary processes.
method Encoder-decoder neural network structure to model past observations.
result Upper bounds for forecast error under specific assumptions.
Paper presents a method for imputing and forecasting structural response from incomplete sensor data.
problem Missing sensor data in structural health monitoring (SHM).
method Incremental Bayesian tensor learning for spatiotemporal missing data reconstruction and forecasting.
result The proposed method achieves accurate and robust imputation and prediction even with high rates of missing data.
New method forecasts multilinear data using tensor autoregression.
problem Forecasting 2D data in big data.
method L-Transform Tensor autoregressive (L-TAR) method.
result Statistical independence achieved through invertible discrete linear transforms.
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of …
Study improves seasonal forecasts using deep learning.
problem Challenges in generating large forecast ensembles and limited observations for verification.
method Developed a probabilistic deep neural network model.
result Demonstrated favorable skill compared to state-of-the-art dynamical forecast systems.
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
problem Calibrated uncertainty quantification in probabilistic weather forecasts
method Conformal prediction
result Calibrated uncertainty at no expense to other probabilistic metrics
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
The study provides statistical theory for WGANs in time series forecasting.
problem Statistical analysis of WGANs for time series forecasting.
method Statistical theory and upper bounds for excess Bayes risk, weak convergence, and confidence intervals.
result Developed confidence intervals for time series forecasting using WGANs.
Neural networks learn from ensemble forecasts without considering their order.
problem Improving reliability of probabilistic weather forecasts.
method Permutation-invariant neural networks for postprocessing ensemble forecasts.
result Models achieve state-of-the-art prediction quality in surface temperature and wind gust forecasts.
A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption. To improve forecasting performance, traditional approaches usually require additional feature sets. However, adding more feature sets from differe…
Long Short-Term Memory (LSTM) is a well-known method used widely on sequence learning and time series prediction. In this paper we deployed stacked LSTM model in an application of weather forecasting. We propose a 2-layer spatio-temporal stacked LSTM model which consists of independent LSTM models per location in the f…
Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method uti…
PBC improves AI and dynamical subseasonal forecasts by reducing biases.
problem Subseasonal forecast accuracy drops due to model biases and compounding errors.
method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.
result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.
Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature s…
FNOs improve spatio-temporal forecasting without needing PDE details.
problem Complex spatio-temporal dynamics in physical and biological phenomena.
method Fourier Neural Operators (FNOs) for dynamic spatio-temporal modeling.
result FNO forecasts are accurate and capture complex real-world dependencies.
New algorithms improve time series prediction with uncertainty quantification.
problem Uncertainty quantification for time series prediction.
method Combines conformal prediction and control theory for online, adaptive forecasting.
result Improves coverage over ensemble forecasters in real-world applications.
Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary tempor…
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a…
Paper introduces a neural framework for accurate energy forecasting.
problem Challenges of forecasting energy demand and supply due to variability of renewable sources and dynamic consumption patterns.
method Integrates Neural ODEs, graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies.
result Consistently outperforms state-of-the-art baselines in various forecasting metrics across diverse datasets.
The paper presents a machine learning framework to combine weather forecasts from multiple models.
problem Combining forecasts from different NWP models with varying biases and limitations.
method Three-stage framework using Quantile Regression Forests and quantile averaging.
result The framework generates well-calibrated probabilistic weather forecasts suitable for decision support.
Neural network-based post-processing improves ensemble forecast sharpness
problem Reducing the width of central prediction intervals in ensemble forecasts
method Extending loss function with a penalty term
result 8.2%-12.5% reduction in width of central prediction interval
Improved visibility forecasts using statistical post-processing.
problem Accurate and reliable predictions of visibility are crucial in aviation and transportation.
method Calibrated ensemble forecasts using locally, semi-locally, and regionally trained POLR and MLP classifiers.
result Post-processing improves forecast skill and POLR models outperform MLPs.
OceanForecastBench offers a comprehensive benchmark for data-driven ocean forecasting models.
problem Lack of open-source, standardized benchmarks for data-driven ocean forecasting models.
method Proposes OceanForecastBench, a benchmark with high-quality data and evaluation pipeline.
result Offers the most comprehensive benchmarking framework for data-driven ocean forecasting.
Improved weather forecasting using deep CNN on cubed-sphere grid.
problem Global weather prediction accuracy and speed.
method Deep convolutional neural network (CNN) on cubed-sphere grid, offline mapping, loss minimization.
result Significantly improved weather forecasts, indefinitely stable, realistic patterns at long lead times.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
problem Predicting pyrocumulonimbus clouds to adapt to climate change.
method Database of 148 pyroCb events, Random Forests, CNNs, and Auto-Encoders.
result Best model predicts pyroCb with 90% accuracy.
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
problem Accurate mid-term hourly load forecasting for power plant operation and energy management.
method Generalized Additive Models (GAMs) with P-splines and autoregressive post-processing.
result Significantly enhanced forecasting accuracy compared to state-of-the-art methods.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
Neural network predicts weather probabilities with added uncertainty.
problem Inferring uncertainty from weather predictions.
method Data-driven neural network trained on WeatherBench dataset, combining outputs from subsets of variables.
result More accurate than some numerical models, provides probabilistic information.
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our propose…
Machine learning predicts seasonal precipitation for East Africa.
problem Predicting seasonal precipitation for East Africa using machine learning.
method Dimension reduction via EOFs, large-scale climate variability indices as features, interpretable ML algorithm.
result The ML approach shows significant positive skill in predicting precipitation for OND season, comparable to ECMWF forecasts.
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose sup…
The study models and forecasts natural gas prices using skewed, heavy-tailed distributions.
problem Modeling and forecasting natural gas prices with heavy tails and conditional heteroscedasticity.
method State-space time series models under skewed, heavy-tailed distributions.
result The proposed model reduces out-of-sample CRPS by 13% for Day-Ahead and 9% for Month-Ahead forecasts.
Forecast dam inflow using sea surface feature weights.
problem Accurate dam inflow forecasting for flood mitigation.
method Extracted sea surface features, applied L2-norm ensemble weighting, used PCA and t-SNE for dimensionality reduction, and calibrated regression models.
result The proposed method improves predictor stability and accuracy in dam inflow forecasting.