Unified framework for generating meteorological time series from text.
problem Lack of large-scale, physically grounded multimodal datasets and architectures ignoring spectral-temporal structure.
method Introduce MeteoCap-3B dataset and MTransformer model.
result State-of-the-art generation quality, accurate cross-modal alignment, strong semantic controllability.
Develops an anomaly detection system using synthetic data.
problem Automatic anomaly detection for meteorological time-series.
method Constructs an ensemble of anomaly detectors using synthetic data and adaptive threshold selection.
result Demonstrates the efficiency of the method in a real-world application.
RainfallBench benchmarks GNSS-based precipitation nowcasting models, addressing complex meteorological challenges.
problem Evaluation of precipitation nowcasting models in meteorology is insufficient due to focus on periodic variables.
method RainfallBench dataset and specialized evaluation protocols for multi-scale, multi-resolution, and extreme rainfall events.
result Bi-Focus Precipitation Forecaster (BFPF) enhances rainfall time series forecasting by incorporating domain-specific priors.
Study predicts droughts using ANN models and hydro-meteorological data.
problem Accurate prediction of short and long-term droughts.
method Employed Artificial Neural Network (ANN) models to predict droughts using SPI at different time scales and various hydro-meteorological variables.
result Hydro-meteorological variables significantly improve SPI prediction at different time scales.
PeakWeather provides Swiss weather station data for machine learning.
problem Accurate weather forecasting for various activities and decision-making.
method High-quality dataset of surface weather observations from 8 years of Swiss stations.
result Dataset supports a wide range of spatiotemporal tasks.
Probabilistic NDVI forecasting from sparse satellite data.
problem Challenges in short-term NDVI forecasting due to sparse and irregular satellite data.
method Probabilistic forecasting framework using historical NDVI and meteorological observations, with temporal-distance weighted quantile loss and extreme-weather feature engineering.
result The proposed method outperforms baselines on pointwise and probabilistic evaluation metrics.
New algorithm for uncertain time series classification.
problem Uncertainty in time series data.
method Uncertain dissimilarity measure based on Euclidean distance and uncertain shapelet transform.
result Effectiveness of the uncertain shapelet transform algorithm on state-of-the-art datasets.
RDIS fills missing values in time series data explicitly.
problem Missing values in time series data.
method Random Drop Imputation with Self-training.
result RDIS achieves competitive results on real-world datasets.
KEDformer improves long-term time series forecasting with seasonal-trend decomposition.
problem Accurate long-term predictions in energy, finance, and meteorology.
method Knowledge extraction-driven framework integrating seasonal-trend decomposition.
result KEDformer enhances model's ability to capture short-term and long-term patterns.
Paper benchmarks and customizes energy forecasting methods.
problem Energy forecasting challenges and differences from traditional time series.
method Collected large-scale load datasets and renewable energy datasets. Developed feature engineering and customized loss functions.
result Comprehensive evaluation of 21 forecasting methods in energy datasets.
LAVARNET predicts multivariate time series by estimating causal variable relationships.
problem Forecasting multivariate time series requires understanding causal interrelationships among variables.
method LAVARNET is a neural network architecture that estimates causal effects and predicts future values.
result LAVARNET outperforms other models on various real-world data sets.
In this paper, an optimal inequality involving the delta curvature is exposed. An application of Riemannian submersions dealing meteorology is presented. Some characterizations about the vertical motion and the horizontal divergence are obtained.
In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data. It is critical to efficiently identify the potential periodic patterns from mas…
We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies across variables. Conditional Chow-Liu tree models are introduced, as an extension to standard Chow-Liu trees, for modeling conditional rather th…
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
problem Cold-start PV forecasting
method Zero-shot pipeline with synthetic histories
result TabPFN-TS achieves the lowest error under Real Feedback strategy
Bayesian framework selects features and lags for time series forecasting.
problem Variable selection and lagged error term identification in time series models.
method Hierarchical Bayesian models with spike-and-slab priors, two-stage MCMC algorithm.
result Posterior selection consistency under mild conditions, improved predictive performance.
A new model predicts wind speed using multiple meteorological variables.
problem Precise wind speed forecasting for wind power producers and grid operators.
method Multi-variable Stacked Long Short-Term Memory (LSTM) network.
result The proposed MSLSTM model outperforms traditional methods in wind speed prediction.
A deep learning model corrects precipitation bias without expert knowledge.
problem Precipitation bias in numerical predictions due to limited observation and models.
method Data-driven deep learning model with Denoising Autoencoder and Ordinal Regression blocks.
result The model achieves the best correcting performance and TS compared to classical methods.
New method clusters hydrological and sediment data for storm event analysis.
problem Analyzing storm events for water quality constituents like turbidity.
method Multivariate time series clustering of river discharge and sediment data.
result Clusters differ from 2-D hysteresis loop classifications.
CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.
problem Pervasive missing values degrade multivariate time series forecasting accuracy.
method CoIFNet integrates imputation and forecasting through Cross-Timestep Fusion and Cross-Variate Fusion modules.
result CoIFNet achieves 24.40% improvement over state-of-the-art methods at 0.6 point (block) missing rate.
PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.
problem Efficient pattern detection in large, multi-track sequential data with high variance and lack of ground truth.
method Query-aware locality-sensitive hashing for feature learning, sub-linear training and inference time.
result PSEUDo achieves sub-linear time efficiency for pattern modeling and comparison of 10,000 multivariate time series.
Machine learning predicts air quality in urban areas.
problem Data sparsity and missing labels in air quality monitoring datasets.
method A machine learning-based method using a neural network enhanced with NARX for time series prediction.
result The proposed method accurately predicts Air Quality Index (AQI) with robust performance across different urban areas.
Proposes a virtual bidding strategy for electricity markets using stochastic control.
problem Optimizing electricity prices in day-ahead and real-time markets.
method Modeling price differences as Brownian motion with meteorological variables, transforming into portfolio management problem.
result Developed a strategy to manage electricity prices efficiently.
Paper proposes a method to identify wind hazard types and predict extreme wind speeds.
problem Difficulty in identifying wind hazard types from meteorological data records.
method Numerical pattern recognition method with feature extraction and generalization.
result Algorithm performance validated using K-fold cross-validation and real-world data.
LGnet jointly models local and global dynamics for MTS forecasting with missing values.
problem Missing values in multivariate time series data.
method LGnet framework using memory network and adversarial training.
result LGnet effectively forecasts MTS with missing values and robust under various missing ratios.
Gaussian Process Regression accurately models daily pan evaporation in humid climates.
problem Precise estimation of pan evaporation in humid climates using data-based methods.
method Gaussian Process Regression and other machine learning techniques were used to estimate pan evaporation.
result GPR models with specific meteorological parameters performed best in estimating pan evaporation.
Study analyzes climate impact on agricultural prices, offering insurance solutions.
problem Financial risk from climate-induced agricultural price volatility.
method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.
result Improved agricultural risk modeling and insurance mechanisms.
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental sci…
STConvS2S improves weather forecasting using only convolutional layers.
problem Predicting future weather conditions more accurately.
method Proposes a deep learning architecture combining spatiotemporal convolutional layers.
result Outperforms state-of-the-art architectures for forecasting tasks.
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised cluste…
Dataset for rainfall modeling in central Europe from 1981-2011.
problem Improving rainfall streamflow modeling beyond simple catchments.
method Spatially resolved meteorological and ancillary data compilation.
result Dataset for neural network-driven hydrological modeling.
Study assesses risk of upward lightning at wind turbines using direct measurements and machine learning.
problem Risk underestimation of upward lightning at wind turbines due to limited detection by current standards.
method Direct UL measurements linked to meteorological reanalysis data using random forests.
result Risk maps based on case study events show high probabilities coincide with actual UL events.
Phase2vec learns embeddings of dynamical systems without supervision.
problem Predicting the classes of 2D dynamical systems from data.
method Physics-informed convolutional network that extracts geometric features and minimizes reconstruction loss.
result Learned embeddings respect the semantics of physical systems and outperform blackbox classifiers.
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.
Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.
problem Uncertainty in measuring aerosol vertical distributions due to limited observations.
method Bayesian nonparametric Gaussian process modeling using meteorological predictors.
result Model reconstructs realistic extinction profiles with well-calibrated uncertainty, outperforming idealized baselines.
Enhanced visibility forecasts using CAMS data improve accuracy.
problem Improving the accuracy of visibility predictions in weather forecasts.
method Statistical post-processing with historical observations and CAMS forecasts.
result Post-processed forecasts with CAMS data are substantially superior to raw and climatological predictions.
Study improves distributional regression evaluation with CRPS, finding optimal rates of convergence.
problem Improving probabilistic forecasts in meteorology using distributional regression.
method Extends theoretical properties of CRPS evaluation to include covariates and finite sample sizes, analyzing convergence rates for different methods.
result Optimal minimax rate of convergence for distributional regression methods is achieved by k-nearest neighbor and kernel methods.
GATGPT uses LLMs with graph attention for spatiotemporal data imputation.
problem Missing values in spatiotemporal data due to sensor malfunctions and data transmission errors.
method Integrates pre-trained large language models with graph attention mechanisms.
result GATGPT achieves comparable results to deep learning benchmarks on real-world datasets.
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.
Study evaluates how limited training data affects streamflow predictions.
problem Limited historical meteorological and streamflow data affects streamflow prediction accuracy.
method Evaluated tree- and LSTM-based models on CAMELS dataset with varying training data sizes and time spans.
result Tree- and LSTM-based models provide similarly accurate predictions on small datasets, but LSTMs are superior with more training data.
New neural networks model for spatio-temporal data.
problem Building a mapping from spatially encoded time series covariates to real-valued response data.
method Proposed two novel extensions of Functional Neural Network (FNN) for spatio-temporal regression.
result Demonstrated effectiveness in handling varying spatial correlations through comprehensive simulation studies.
Prequential posteriors tackle data assimilation for deep generative forecasting models.
problem Challenges in assimilating data into deep generative forecasting models due to intractable likelihood functions.
method Introduces prequential posteriors based on a predictive-sequential loss function, proving consistency under mild conditions, and using parallelizable SMC samplers for scalable inference.
result Prequential posteriors concentrate around parameters with optimal predictive performance, validating method on synthetic and real-world datasets.
Improved hydrological models using LSTM networks on large datasets.
problem Traditional hydrological models degrade when applied to multiple basins.
method Long Short-Term Memory (LSTM) networks trained on 531 basins from CAMELS data.
result Single LSTM model outperforms regional and basin-specific models.
FLUXCOM merges eddy covariance data with remote sensing to estimate global energy fluxes.
problem Poorly constrained global land-atmosphere energy fluxes.
method Machine learning to merge eddy covariance data with remote sensing and meteorological data.
result Estimates of net radiation, sensible heat, and evapotranspiration with uncertainties.
We propose a betting strategy based on Bayesian logistic regression modeling for the probability forecasting game in the framework of game-theoretic probability by Shafer and Vovk (2001). We prove some results concerning the strong law of large numbers in the probability forecasting game with side information based on …
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.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
problem Predicting customized allergy seasons for individual patients.
method Triple-regression algorithm with pre-processing and three-stage regressions.
result Improved forecasting accuracy and reduced uncertainty.
Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…