Generative machine learning methods for multivariate ensemble
post-processing
- URL: http://arxiv.org/abs/2211.01345v2
- Date: Thu, 1 Feb 2024 08:29:55 GMT
- Title: Generative machine learning methods for multivariate ensemble
post-processing
- Authors: Jieyu Chen, Tim Janke, Florian Steinke, Sebastian Lerch
- Abstract summary: We present a novel class of nonparametric data-driven distributional regression models based on generative machine learning.
In two case studies, our generative model shows significant improvements over state-of-the-art methods.
- Score: 2.266704492832475
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Ensemble weather forecasts based on multiple runs of numerical weather
prediction models typically show systematic errors and require post-processing
to obtain reliable forecasts. Accurately modeling multivariate dependencies is
crucial in many practical applications, and various approaches to multivariate
post-processing have been proposed where ensemble predictions are first
post-processed separately in each margin and multivariate dependencies are then
restored via copulas. These two-step methods share common key limitations, in
particular the difficulty to include additional predictors in modeling the
dependencies. We propose a novel multivariate post-processing method based on
generative machine learning to address these challenges. In this new class of
nonparametric data-driven distributional regression models, samples from the
multivariate forecast distribution are directly obtained as output of a
generative neural network. The generative model is trained by optimizing a
proper scoring rule which measures the discrepancy between the generated and
observed data, conditional on exogenous input variables. Our method does not
require parametric assumptions on univariate distributions or multivariate
dependencies and allows for incorporating arbitrary predictors. In two case
studies on multivariate temperature and wind speed forecasting at weather
stations over Germany, our generative model shows significant improvements over
state-of-the-art methods and particularly improves the representation of
spatial dependencies.
Related papers
- Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation
for Time Series [49.992908221544624]
Time series data often exhibit numerous missing values, which is the time series imputation task.
Previous deep learning methods have been shown to be effective for time series imputation.
We propose a non-generative time series imputation method that produces accurate imputations with inherent uncertainty.
arXiv Detail & Related papers (2023-12-03T05:52:30Z) - Structured Radial Basis Function Network: Modelling Diversity for
Multiple Hypotheses Prediction [51.82628081279621]
Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions.
A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems.
It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution.
arXiv Detail & Related papers (2023-09-02T01:27:53Z) - Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting [10.083361616081874]
This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting.
Lagged numerical ensemble forecasts and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods.
For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models.
arXiv Detail & Related papers (2022-11-29T01:11:04Z) - Bayesian Sparse Regression for Mixed Multi-Responses with Application to
Runtime Metrics Prediction in Fog Manufacturing [6.288767115532775]
Fog manufacturing can greatly enhance traditional manufacturing systems through distributed computation Fog units.
It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification of runtime performance metrics.
We propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics.
arXiv Detail & Related papers (2022-10-10T16:14:08Z) - Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF)
Our model avoids the influence of cumulative error and does not increase the time complexity.
Our model achieves state-of-the-art performance on many popular multivariate datasets.
arXiv Detail & Related papers (2022-05-16T07:53:42Z) - TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
We propose a versatile method that estimates joint distributions using an attention-based decoder.
We show that our model produces state-of-the-art predictions on several real-world datasets.
arXiv Detail & Related papers (2022-02-07T21:37:29Z) - Machine Learning for Multi-Output Regression: When should a holistic
multivariate approach be preferred over separate univariate ones? [62.997667081978825]
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods.
We compare these methods in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.
arXiv Detail & Related papers (2022-01-14T08:44:25Z) - CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting [70.54920804222031]
We propose a general probabilistic multi-view forecasting framework CAMul.
It can learn representations and uncertainty from diverse data sources.
It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner.
We show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25% in accuracy and calibration.
arXiv Detail & Related papers (2021-09-15T17:13:47Z) - A similarity-based Bayesian mixture-of-experts model [0.5156484100374058]
We present a new non-parametric mixture-of-experts model for multivariate regression problems.
Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point.
Posterior inference is performed on the parameters of the mixture as well as the distance metric.
arXiv Detail & Related papers (2020-12-03T18:08:30Z) - Multivariate Probabilistic Time Series Forecasting via Conditioned
Normalizing Flows [8.859284959951204]
Time series forecasting is fundamental to scientific and engineering problems.
Deep learning methods are well suited for this problem.
We show that it improves over the state-of-the-art for standard metrics on many real-world data sets.
arXiv Detail & Related papers (2020-02-14T16:16:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.