Deep learning improves PV generation quantile forecasting.
problem Accurate probabilistic forecasting of PV generation.
method Developed an encoder-decoder deep learning model for multi-output quantile PV forecasting.
result The model improves forecast quality and computational efficiency.
Deep models forecast epidemics with uncertainty quantification.
problem Accurate probabilistic forecasting of epidemics is challenging due to nonlinear temporal dependencies and spatial interactions.
method Deep spatiotemporal engression methods with geometric ergodicity and asymptotic stationarity.
result Proposed methods outperform benchmarks in point and probabilistic forecasting.
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.
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.
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.
Deep learning models outperform classical methods in forecasting neural activity.
problem Improving forecasting of neural activity using deep learning models.
method Systematic evaluation of eight probabilistic deep learning models against classical statistical models and baseline methods.
result Several deep learning models consistently outperform classical approaches in forecasting neural activity.
A new framework evaluates deep learning vs classical forecasting methods for time series predictions.
problem Current forecasting model evaluation metrics fail to capture model performance differences.
method Proposes a novel framework for evaluating univariate time series forecasting models from multiple perspectives.
result Deep learning models like NHITS outperform classical methods in multi-step ahead forecasting but not in anomaly handling.
Deep learning models improve financial price forecasting accuracy.
problem Accurately predicting financial time series prices.
method Review of recent advancements in deep learning models for price forecasting.
result Deep learning models outperform traditional methods in financial price forecasting.
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.
We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated…
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
Deep learning model forecasts PV power production with high accuracy.
problem Accurate forecasting of photovoltaic power production is needed for electricity infrastructure.
method Sequence to sequence model with attention mechanism using weather predictions and historical data.
result The model outperforms existing methods in forecast skill score.
This paper studies uncertainty quantification in deep spatiotemporal forecasting.
problem Uncertainty quantification in deep spatiotemporal forecasting models.
method Analysis of UQ methods from Bayesian and frequentist perspectives, including statistical decision theory.
result Different UQ methods have different strengths and weaknesses, with Bayesian methods being more robust in mean prediction and frequentist methods providing more extensive coverage.
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.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
problem Efficiently forecasting millions of time series with high accuracy.
method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.
result Significant reduction in training time and memory usage with comparable accuracy.
Paper proposes a method for predicting any quantile of short-term electricity demand.
problem Uncertainty in power systems due to multiple factors.
method Proposes a novel general approach for distributional forecasting of short-term electricity demand.
result Demonstrates state-of-the-art distributional forecasting results for short-term electricity demand.
The paper compares DL models to WP curve modeling for forecasting with irregular shutdowns.
problem Forecasting wind power with irregular shutdowns due to redispatching.
method Compared autoregressive DL models to WP curve modeling.
result WP curve modeling achieves lower forecasting errors and is more computationally efficient.
Proposes a neural network for accurate and reconciled hierarchical time series forecasting.
problem Forecasting and reconciling hierarchical time series data.
method Uses a deep neural network to directly produce accurate and reconciled forecasts, minimizing a customized loss function at training time.
result Our approach outperforms state-of-the-art competitors in hierarchical forecasting on real-world datasets.
Deep learning models forecast stock market orders over multiple time frames.
problem Forecasting stock market orders over varying time frames.
method Encoder-decoder models with sequence-to-sequence and Attention mechanisms, leveraging Intelligent Processing Units (IPUs) for faster training.
result Multi-horizon forecasting outperforms single-horizon models, especially for long prediction periods.
WindDragon forecasts wind power with deep learning.
problem Accurate short-term wind power forecasting is crucial for grid operation.
method Automated Deep Learning combined with Numerical Weather Predictions.
result Automated Deep Learning improves wind power forecasting accuracy.
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.
RankNet forecasts car racing positions with improved accuracy and stability.
problem Forecasting rank positions in car racing, especially considering pit stops.
method Cause-effect decomposition in RankNet, incorporating probabilistic forecasting.
result RankNet outperforms baselines significantly, improving MAE by over 10%.
This paper introduces a deep learning ensemble forecasting model using Dirichlet process.
problem Forecasting with deep learning ensemble models.
method Infinite mixture model based on Dirichlet process, with decaying learning rate strategy.
result The ensemble model outperforms single benchmark models in prediction accuracy and stability.
Deep learning improves solar energy forecasting using physical and data-driven models.
problem Improving short-term solar energy forecasting accuracy.
method Injecting physical knowledge into deep learning models for spatio-temporal forecasting.
result Improved solar energy forecasting models using deep learning and physical criteria.
Paper introduces normalizing flows for accurate probabilistic energy forecasting.
problem Uncertainty in renewable energy forecasting for power systems.
method Normalizing flows for direct learning of multivariate stochastic distributions.
result Normalizing flows outperform other deep learning models in probabilistic forecasting.
MES-LSTM hybrid method improves multivariate time series forecasting and mortality modeling.
problem Challenges in applying hybrid forecast methods to multivariate data.
method Generalized multivariate extension of ES-RNN, utilizing vectorized implementation.
result MES-LSTM shows significant improvement over pure statistical and deep learning methods in forecast accuracy and prediction interval construction.
Survey of deep learning methods for time series forecasting.
problem Improving accuracy in time series predictions across various domains.
method Analysis of common encoder and decoder designs, hybrid models, and decision support.
result Advancements in deep learning for time series forecasting.
Deep models predict intraday electricity prices accurately.
problem Accurately forecasting intraday electricity prices.
method Two deep time series probabilistic models using ESNs with stochastic disturbances and copulas.
result Deep distributional models provide accurate short-term probabilistic price forecasts.
Deep learning predicts employment changes and industry health.
problem Forecasting short-term employment changes and assessing long-term industry health.
method LSTNet, a multi-scale deep learning model, processes multivariate time series data.
result LSTNet outperforms baseline models in most sectors, especially stable ones.
The study compares econometric and deep learning models for forecasting COMEX copper futures volatility.
problem Forecasting volatility of COMEX copper futures across different time intervals.
method Econometric models (GARCH, HAR) and deep learning models (RNN, LSTM, GRU) applied to daily and hourly data.
result Deep learning models outperform econometric models in hourly data, but HAR remains the best overall for daily data.
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
problem Recent deep learning models often outperform multivariate ones in MTSF.
method CATS constructs ATS from OTS using a 2D temporal-contextual attention mechanism.
result CATS achieves state-of-the-art performance with reduced complexity.
Deep state space model forecasts time series with uncertainty.
problem Probabilistic forecasting for risk management.
method Parameterized deep networks for non-linear models, recurrent neural nets for dependency, ARD network for exogenous variables.
result Accurate and sharp probabilistic forecasts with realistic uncertainty growth.
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.
Unified deep learning approach for time series forecasting using VMD-CNN-LSTM.
problem Time series forecasting problem.
method Proposes a unified deep learning approach with decomposition-reconstruction-ensemble framework using VMD-CNN-LSTM.
result The proposed approach outperforms benchmark approaches in forecasting accuracy.
Proposes a model for multi-horizon probabilistic forecasting of time series influenced by asynchronous events.
problem Forecasting time series influenced by asynchronous events is challenging.
method Introduces Variational Synergetic Multi-Horizon Network (VSMHN), a deep conditional generative model combining deep point processes and variational recurrent neural networks.
result Produces accurate, sharp, and realistic probabilistic forecasts.
Deep learning models predict call center volumes with seasonal patterns.
problem Forecasting call center volumes with complex seasonal behavior.
method Investigated recurrent neural networks (RNNs) including Elman, LSTM, and GRU models.
result Optimal RNN configurations outperform other forecasting techniques.
A novel deep probabilistic model for dynamic systems forecasting.
problem Probabilistic forecasting in dynamic systems.
method Combining deep generative models and state space models with recurrent neural networks and variational sequence models.
result Outperforms existing models in system identification benchmarks and real-world centrifugal compressor forecasting.
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.
A deep learning method for probabilistic weather forecasting.
problem Probabilistic forecasting of weather.
method Two chained machine-learning steps: dimension reduction and density estimation using normalizing flows.
result The method produces accurate conditional forecast distributions for weather.
Structured subsampling improves privacy in deep time series forecasting.
problem Incompatible privacy guarantees with time series forecasting.
method Structured subsampling of sequential data for privacy amplification.
result Structured subsampling enables training with strong privacy guarantees.
Proposes DILATE and STRIPE++ for precise time series forecasting.
problem Non-stationary signals with sudden changes.
method Incorporates shape and temporal criteria in deep learning models.
result Improves precision in deterministic and probabilistic forecasting.
Proposes a neural network for efficient imbalance electricity price forecasting.
problem Accurate and efficient imbalance electricity price forecasting in industrial energy trading systems.
method Market-rule-informed neural network framework.
result The proposed model achieves competitive forecasting performance with fewer parameters and shorter training time.
Transformer model forecasts electricity price spread for virtual bidding.
problem Volatility in renewable energy causes price forecasting challenges.
method Transformer-based deep learning model using various time-series features.
result Trading strategy at peak hour yields nearly consistent profit.
Online data augmentation improves forecasting performance in deep learning.
problem Insufficient training data for forecasting tasks.
method An online data augmentation framework that generates synthetic samples during training.
result Online data augmentation leads to better forecasting performance.
Boosted embeddings improve time series forecasting models.
problem Improving time series forecasting accuracy.
method Gradient boosting with DNN weak learners and incremental weight updates.
result DeepGB model outperforms state-of-the-art models.
A shallow Bi-LSTM model forecasts Bitcoin prices using engineered features.
problem Lack of high-quality historical Bitcoin data for deep learning models.
method Proposed a shallow Bi-LSTM model with feature engineering on Bitcoin data.
result A shallow deep neural network outperforms other models in Bitcoin price forecasting.
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors …
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.