Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Proposes a framework to explain complex global forecasting models.
problem Lack of interpretability in global forecasting models reduces stakeholder trust.
method Trains simpler univariate surrogate models on local forecasts of global models.
result Shows improved local model-agnostic interpretability of global forecasting models.
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.
Global models outperform local models in forecasting intermittent time series.
problem Forecasting intermittent time series with zeros in supply chains.
method Comparison of state-of-the-art probabilistic local and global models on five datasets.
result TiDE, a simple neural network architecture, achieves the best accuracy among global models.
Global neural networks improve financial forecasting accuracy with larger, diverse datasets.
problem Mixed empirical performance in financial time series forecasting due to local model estimation.
method Global estimation strategy that pools information across cross-sections of over 10,000 global stocks.
result Forecasting accuracy improves with larger and more heterogeneous training datasets.
Global models outperform univariate benchmarks in complex time series forecasting.
problem Comparing global forecasting models to univariate benchmarks in various challenging scenarios.
method Simulated datasets with controlled characteristics, including homogeneity, complexity, and series lengths. Global forecasting models (RNN, LGBM) compared to univariate techniques.
result Global models like RNN and LGBM are competitive in complex scenarios with short series lengths and heterogeneous data.
Global methods outperform local in forecasting groups of time series, even in heterogeneous datasets.
problem Forecasting groups of time series, especially in heterogeneous datasets.
method Local methods consider each series separately, global methods fit a single model to all series.
result Global methods can outperform local methods in forecasting groups of time series, even in heterogeneous datasets.
Study compares local and global models for hierarchical forecasting accuracy.
problem Challenges in hierarchical time series forecasting, especially in accuracy and information utilisation.
method Developed and evaluated local and global forecasting models (GFMs) to exploit cross-series and cross-hierarchies information.
result Global Forecasting Models (GFMs) outperform local models in hierarchical forecasting accuracy and computational efficiency.
Graph Signal Processing improves stock market volatility forecasting.
problem Forecasting realized volatility in a global stock market context.
method Integrating Graph Signal Processing into the HAR model.
result The proposed model outperforms HAR-type benchmarks.
Study uses zero-shot models to forecast mortality rates globally.
problem Forecasting mortality rates without task-specific fine-tuning.
method Two state-of-the-art foundation models (TimesFM and CHRONOS) and traditional/machine learning methods were evaluated.
result CHRONOS outperformed traditional methods for shorter-term forecasts, but TimesFM consistently underperformed.
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
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.
Meta-GLAR combines global deep representations with local adaptation for improved forecasting accuracy.
problem Joint learning from related time series boosts accuracy but fails for out-of-sample forecasting.
method Meta-GLAR uses a meta-learning approach to adapt RNN representations for each time series.
result Meta-GLAR outperforms state-of-the-art methods in out-of-sample forecasting accuracy.
Less frequent retraining improves forecast accuracy in retail demand forecasting.
problem Balancing forecast accuracy and computational efficiency in global models.
method Analysis of ten machine learning and deep learning models across two large retail datasets with various retraining scenarios.
result Less frequent retraining strategies maintain forecast accuracy while reducing computational costs.
Forecast predicts US recession in 2017, global economic slowdown, and eventual growth.
problem Short-term economic forecast and potential recession in developed countries.
method Analysis of log-periodic oscillations in DJIA dynamics and historical economic cycles.
result Predicts a recession in the second half of 2017 for developed countries.
DeepGLO forecasts high-dimensional time series by combining global and local models.
problem Forecasting high-dimensional time series with global patterns and local calibration.
method Hybrid model combining global matrix factorization and local temporal networks.
result DeepGLO outperforms state-of-the-art approaches by more than 25% in WAPE.
TelePiT improves S2S forecasting by integrating physics and teleconnections.
problem Challenges in subseasonal-to-seasonal climate forecasting due to chaotic dynamics and complex interactions.
method Integrates physics and teleconnections into a transformer architecture with spherical embedding and multi-scale physics-informed neural ODE.
result Significantly outperforms state-of-the-art methods across all forecast horizons.
Optimized DMD for fast atmospheric chemistry forecasting.
problem Forecasting global atmospheric chemistry dynamics efficiently.
method Optimized Dynamic Mode Decomposition (DMD) for reduced order modeling.
result Significant improvement in computational speed and interpretability.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Paper proposes a forecasting model combining autoregressive models with spectral attention.
problem Time series forecasting across various domains.
method Combines deep autoregressive models with Spectral Attention (SA) module.
result SAAM consistently demonstrates improved forecasting accuracy compared to state-of-the-art approaches.
Deep generative models improve global precipitation forecasts.
problem Accurately forecasting extreme rainfall is challenging and costly.
method Trained a Conditional Generative Adversarial Network (CorrectorGAN) to correct and super-resolve global precipitation forecasts.
result CorrectorGAN produces high-resolution, bias-corrected forecasts in seconds.
A novel tree algorithm improves time series forecasting accuracy.
problem Improving accuracy in non-linear time series forecasting.
method Developed a hierarchical TAR model as a regression tree that trains globally across series, introducing a forecasting-specific tree algorithm with cross-series learning.
result Significantly higher accuracy than state-of-the-art tree-based algorithms and benchmarks across four metrics.
Archive of 20 time series datasets for forecasting evaluation.
problem Lack of comprehensive time series forecasting datasets.
method Compilation and characterisation of 20 datasets from various domains.
result Characterisation and performance evaluation of datasets.
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.
Generative model emulates climate model for 100-year forecasts.
problem Challenges in accurately simulating long-term climate data.
method Integrates DYffusion with SFNO for stable, accurate climate simulations.
result Achieves near gold-standard performance for climate model emulation.
Deep learning models forecast multiple yield curves with improved accuracy.
problem Globalization of financial markets affects yield curves.
method Combines self-attention mechanism and nonparametric quantile regression.
result Effective point and interval forecasts of future yields.
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.
New method uses nearest neighbors quantile filter for probabilistic energy forecasting.
problem Creating accurate probabilistic energy forecasts using complex data mining techniques.
method Uses a new nearest neighbors quantile filter to create quantile regressions without a non-differentiable cost function.
result Demonstrates superior performance in Global Energy Forecasting Competition 2014.
A hybrid loss framework improves time series forecasting by balancing global and component errors.
problem Current time series methods may prioritize less significant sub-series, leading to forecasting bias.
method Proposes a hybrid loss framework combining global and component losses, dynamically adjusting weights.
result Improves time series forecasting performance by 0.5-2% on average.
The study compares statistical post-processing methods for solar radiation forecasts.
problem Improving accuracy and uncertainty quantification of solar radiation forecasts.
method Statistical post-processing techniques using relationships between meteorological variables and solar radiation.
result Quantile regression and generalized random forests generally perform best in probabilistic forecasts.
ARU adapts deep forecasting models in streaming data with efficient updates.
problem Adapting deep globally trained models for streaming data efficiently.
method ARU combines deep global models with closed-form linear models for per-series adaptation.
result ARU outperforms local adaptation methods on various datasets.
New ensemble methods improve time series forecasting accuracy.
problem Global Forecasting Models (GFM) lack localisation for heterogeneous datasets.
method Ensemble techniques with clustering and varied GFM models.
result Significantly higher accuracy achieved compared to baseline models.
ML improves flood forecasting by leveraging local data.
problem Human calibration, limited data, and computational difficulty.
method Transfer learning and ML for high-dimensional scenarios.
result ML systems achieve timely and accurate flood prediction.
New method improves wind and solar energy forecasts by 48 hours.
problem Volatility of wind and solar energy makes accurate forecasts difficult.
method Two-step machine learning approach to calibrate ensemble forecasts.
result Statistical post-processing improves forecast skill by at least 48 hours.
Study analyzes Airbnb booking lead times during global crises using a new metric.
problem Disruptions in booking behaviors during global crises affect forecasting accuracy.
method Normalized L1 (Manhattan) distance to assess lead time divergences.
result Identified two-phase disruption: abrupt change at pandemic onset followed by partial recovery.
DeepPPMNet forecasts EMS demand and performs causal analyses for policy-making.
problem Accurate prediction and causal analysis of EMS demand for effective policy-making.
method DeepPPMNet, a LSTM-based framework, globally forecasts and analyzes causal relationships using Granger causality.
result DeepPPMNet outperforms traditional methods in forecasting EMS demand and policy-making.
Paper develops flood forecasting system for data scarce regions.
problem Flood forecasting in developing countries with data scarcity.
method Operational system for flood extent forecasts in India.
result Scalable and cost-efficient flood forecasting system.
MQF2 forecasts multivariate quantiles globally.
problem Forecasting multi-horizon dependencies with error accumulation.
method Multivariate quantile function using input-convex neural networks.
result MQF2 avoids quantile crossing and captures time dependency. Paper proposes a new MAR model for global economic forecasting.
problem Joint modeling of economic and financial variables across countries.
method Sparse matrix autoregressive model with trade network integration.
result Sparse component differentiates systematic and idiosyncratic cross-predictability.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
Hybrid model combines deep learning and classical methods for forecasting time series.
problem Forecasting large collections of similar time series is challenging and complex.
method Proposes a hybrid model integrating deep neural networks and classical time series models.
result Demonstrates improved data efficiency, accuracy, and computational complexity.
A new method clusters time series based on model prediction accuracy.
problem Clustering time series data effectively.
method Iterative model fitting and assignment based on predictive accuracy.
result The method outperforms other techniques in clustering and predictive accuracy.
Low-connectivity reservoirs outperform standard designs in chaotic system forecasting.
problem Forecasting chaotic systems with high accuracy and low computational resources.
method Used Bayesian optimization to find optimal reservoir configurations, focusing on global system climate rather than short-term prediction.
result Optimized reservoirs with very low connectivity perform well in forecasting chaotic systems, challenging existing design heuristics.
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
A new method for generating synthetic time series improves forecasting model accuracy.
problem Training forecasting models on imbalanced time series datasets.
method Data augmentation using oversampling strategies for imbalanced learning.
result The proposed method outperforms global and local models.
Deep learning improves probabilistic river discharge forecasting for hydroelectric power.
problem Uncertain river discharges due to climate variability.
method Modified recurrent neural network architecture conditioned on global circulation model projections.
result Generates parameterized probability distributions for realistic long-term discharge scenarios.
Graph-EFM models weather uncertainty with graph-based ensembles.
problem Accurately capturing forecast uncertainty in chaotic weather.
method Flexible latent-variable formulation with hierarchical graph construction.
result Graph-EFM ensembles achieve equivalent or lower errors than deterministic models.
Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting …