Optimal reconciliation keeps some forecasts unchanged in hierarchical forecasting.
problem Keeping some forecasts unchanged in hierarchical forecasting.
method Formulates a method to keep some forecasts unchanged in a hierarchical forecasting system.
result Preserves unbiasedness and non-negativity of forecasts.
Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.
problem Low accuracy in demand forecasts for Knitwear product category.
method Dynamic selection of the best algorithm from an algorithm rack based on performance and context.
result Increased forecast accuracy from 60% to 80% for Knitwear.
New method produces coherent forecasts for long-range data.
problem Inaccurate and non-coherent forecasts on long-horizon data.
method Probabilistic forecasting with KL-divergence for coherent aggregates.
result Improves forecast performance across base levels and aggregates.
Tree-based synthesis improves forecast accuracy in GDP and inflation.
problem Improving forecast accuracy in GDP and inflation.
method Developed a nonparametric synthesis function using regression trees.
result Tree-based synthesis leads to improved forecast accuracy.
Combines VaR and ES forecasts from a large pool of methods.
problem Combining forecasts from a large pool of VaR and ES methods.
method Adapted interval forecast combination methods, including trimmed means and mixtures approach.
result Trimmed mean combinations, mixtures method, and performance-based weighting delivered strong results.
Researchers improve deep ensemble forecast aggregation methods.
problem Aggregating forecast distributions from deep ensembles for better predictive performance.
method Comprehensive analysis of twelve benchmark data sets, comparing probability- and quantile-based aggregation methods for three neural network-based approaches.
result A general quantile aggregation framework for deep ensembles improves predictive performance in various settings.
New method uses shared attention for multi-task time series forecasting.
problem Insufficient training instances in single-task forecasting.
method Self-attention based sharing schemes across multiple tasks.
result Outperforms state-of-the-art single-task forecasting baselines and RNN-based multi-task forecasting method.
Develops forecast hedging for improved calibration of forecasts.
problem Improving the accuracy of forecasted frequencies.
method Combines deterministic and stochastic approaches to forecast hedging.
result Ensures expected track record can only improve.
Paper proposes a new method for selecting the best hierarchical forecasting approach.
problem Selecting the best method for reconciling base forecasts in hierarchical time series.
method Conditional hierarchical forecasting using machine learning and time series features.
result Conditional hierarchical forecasting leads to significantly more accurate forecasts, especially at lower levels.
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…
Proposes a new algorithm for efficient probabilistic reconciliation of forecasts.
problem Ensuring coherence in forecasts for hierarchical time series.
method Bottom-Up Importance Sampling algorithm for any type of forecast distribution.
result Significant improvement over base probabilistic forecasts in experiments.
Time series forecasting models fail to consistently select the best model across different datasets.
problem Inconsistency in model selection for time series forecasting across varying data regimes.
method Characterized time series using descriptors like trend strength, seasonality, noise level, and temporal dependence. Developed a rule-based selection mechanism to map data regimes to candidate models.
result Rule-based model selection achieves low accuracy, with correct model identification occurring in only a small fraction of cases.
New tests for VaR and ES forecast encompassing using flexible link functions.
problem Testing forecast encompassing for Value at Risk and Expected Shortfall.
method Flexible link functions for testing convex forecast combinations and nonstandard asymptotic theory for boundary parameters.
result Tests based on new link functions outperform unrestricted linear link functions for one-step and multi-step forecasts.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
A new hierarchical forecasting method using machine learning improves forecast accuracy.
problem Improving forecast accuracy in hierarchical forecasting systems.
method Non-linear combination of base forecasts, focusing on both accuracy and coherence.
result The proposed method outperforms existing approaches, especially for diverse series.
Time series forecasting has gained lots of attention recently; this is because many real-world phenomena can be modeled as time series. The massive volume of data and recent advancements in the processing power of the computers enable researchers to develop more sophisticated machine learning algorithms such as neural …
An ensemble of randomized NNs improves time series forecasting accuracy.
problem Forecasting time series with multiple seasonality and nonstationarity.
method Randomized neural networks with pattern-based time series representation and diversity control strategies.
result Outperforms statistical and machine learning models in forecasting accuracy.
The study analyzes how probabilistic forecasts improve battery trading strategies in electricity markets.
problem Improvements in statistical forecast quality do not directly translate to economic value in battery trading strategies.
method The study frames battery optimization as a stochastic program based on fully probabilistic forecasts and examines decision quality under different uncertainty models.
result The study identifies two critical flaws in quantile-based trading strategies and provides theoretical justification and empirical evidence.
Enhances traffic forecasting with dynamic regression incorporating error modeling.
problem Improving accuracy of traffic forecasts using deep spatiotemporal models.
method Integrates matrix-variate autoregressive (AR) model into loss function for error series of base model.
result Improved traffic forecasting performance on SOTA models with interpretable AR coefficients.
New methods quantify uncertainties in AI weather forecasts.
problem Uncertainty in AI weather predictions.
method Comparing ensemble and post-hoc uncertainty quantification methods.
result Probabilistic forecasts improve over ensemble physics-based models.
ModelRadar evaluates forecasting models across multiple aspects.
problem Evaluating forecasting models using single scores hides relevant performance variations.
method ModelRadar, a framework for aspect-based evaluation of univariate time series forecasting models.
result NHITS performs best overall but its superiority varies with forecasting conditions.
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.
This paper develops copula-based models for forecasting multivariate realized volatility.
problem Forecasting multivariate realized volatility matrices with hidden dependence structure.
method Copula-based time series models to capture hidden dependence structure and ensure positive definiteness.
result Copula-based models achieve significant performance in volatility matrix forecasting.
MPPN network improves long-term time series forecasting accuracy.
problem Inaccurate long-term time series forecasting due to noise and lack of interpretability.
method MPPN network constructs context-aware multi-resolution semantic units and employs multi-periodic pattern mining and channel adaptive module.
result MPPN significantly outperforms state-of-the-art methods on nine real-world benchmarks.
Robust forecast framework reduces distribution error by 63%.
problem Accurate distribution forecast for planning decisions.
method Backtest-based bootstrap and adaptive residual selection.
result Reduces Absolute Coverage Error by more than 63%.
New method evaluates language model forecasters by checking consistency of predictions.
problem Evaluating the performance of language model forecasters is difficult due to lack of ground truth.
method Developed a consistency check framework based on arbitrage to evaluate forecasters.
result Consistency metrics correlate with ground truth performance of LLM forecasters.
Simplifies forecast combination by using diversity of out-of-sample forecasts.
problem Estimating optimal weights for forecast combinations is challenging.
method Use out-of-sample forecasts to extract features and calculate weights for forecast combination.
result Achieves superior forecasting performance in point forecasts and prediction intervals.
The study improves solar irradiance forecasts for Chile using machine learning.
problem Accurate short-term PV power forecasts for Chile's Atacama Desert.
method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.
result Machine learning-based post-processing methods improve forecast accuracy and calibration.
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 …
A new method forecasts financial tail risks by combining and weighting quantiles.
problem Reducing uncertainty in financial tail risk forecasting.
method Two-step procedure: quantile combination followed by ES computation.
result The proposed framework outperforms individual models and simple approaches.
With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistic…
New neural network model improves long-term financial forecasts.
problem Challenges in forecasting financial time series with limited data.
method Spatiotemporal adaptive neural network using dynamic factor graph and attention-based mechanism.
result Significantly outperforms typical models in forecasting 21-day price trajectories.
The paper evaluates various forecasting methods for inflation, finding ML models superior.
problem Forecasting inflation using disaggregated data and machine learning.
method Examines traditional and machine learning models, including random forest, for disaggregated and aggregated inflation forecasts.
result Aggregating disaggregated forecasts performs similarly to survey-based expectations and aggregate models.
Improved wind speed forecasts for power generation using machine learning.
problem Improving the accuracy and reliability of wind speed predictions for power generation.
method A novel machine learning approach for calibrating wind speed ensemble forecasts.
result The proposed method improves the calibration and accuracy of probabilistic and point forecasts.
Deep learning improves time series forecasting, outperforming other methods.
problem Improving time series forecasting accuracy.
method Deep learning models for time series prediction.
result Deep learning models consistently outperform other methods in forecasting competitions.
Benchmark assesses forecasting models' ability to use textual context.
problem Forecasting models struggle with integrating textual context.
method Introduces a benchmark with numerical and textual data, evaluates various models.
result LLM prompting method outperforms other models.
A new hierarchical forecasting method improves overall accuracy.
problem Hierarchical forecasting challenges, especially for intermittent time series.
method Top-down alignment of independent level forecasts using deep learning and tree-based algorithms.
result Improves overall forecasting accuracy compared to existing methods.
The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem. In addition,…
With the growing prevalence of smart grid technology, short-term load forecasting (STLF) becomes particularly important in power system operations. There is a large collection of methods developed for STLF, but selecting a suitable method under varying conditions is still challenging. This paper develops a novel reinfo…
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…
Graph Neural Networks improve El Niño forecasts.
problem Improving seasonal forecasting models for ENSO.
method Application of spatiotemporal Graph Neural Networks.
result Preliminary results outperform state-of-the-art systems for 1 and 3-month projections.
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
ForecastGAN improves multi-horizon time series forecasting by integrating numerical and categorical features.
problem Limited performance of existing approaches in short-term and long-term forecasting.
method Decomposition, model selection, adversarial training.
result ForecastGAN consistently outperforms state-of-the-art transformer models for short-term forecasting.
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
problem Robust stock index forecasting
method Modified Transformer architecture with Shifted Data Augmentation
result Best performance on benchmark datasets
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.
Novel time series forecasting method using sliding window signatures.
problem Challenges in forecasting nonlinear and delayed time series data.
method Ridge regression with signature features calculated on sliding windows.
result Signature features effectively encode temporal and nonlinear dependencies, leading to accurate forecasts.
EBLR improves time series forecasting with interpretable results.
problem Forecasting future events to reduce uncertainty.
method Iterative method starting with a base model, adding regression trees to explain errors at each iteration.
result EBLR substantially improves base model performance through extracted features and provides comparable performance to other methods.
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.