Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923717" target="_blank" >RIV/00216275:25410/25:39923717 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10479-023-05722-7" target="_blank" >https://link.springer.com/article/10.1007/s10479-023-05722-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-023-05722-7" target="_blank" >10.1007/s10479-023-05722-7</a>

Result language

angličtina

Original language name

Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks

Original language description

Recent trends in global climate modeling, coupled with the availability of more fine-scale datasets, have opened up opportunities for deep learning-based climate prediction to improve the accuracy of predictions over traditional physics-based models. For this, however, large ensembles of data are needed. Generative models have recently proven to be a suitable solution to this problem. For a sound generative model for time-series forecasting, it is essential that temporal dynamics are preserved in that the generated data obey the original data distributions over time. Existing forecasting methods aided by generative models are not adequate for capturing such temporal relationships. Recently, generative models have been proposed that generate realistic time-series data by exploiting the combinations of unsupervised and supervised learning. However, these models suffer from instable learning and mode collapse problems. To overcome these issues, here we propose Wasserstein Time-Series Generative Adversarial Network (WTGAN), a new forecasting model that effectively imitates the dynamics of the original data by generating realistic synthetic time-series data. To validate the proposed forecasting model, we evaluate it by backtesting the challenging decadal climate forecasting problem. We show that the proposed forecasting model outperforms state-of-the- art generative models. Another advantage of the proposed model is that once WTGAN is tuned, generating time-series data is very fast, whereas standard simulators consume considerable computer time. Thus, a large amount of climate data can be generated, which can substantially improve existing data-driven climate forecasting models.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Volume of the periodical

355

Issue of the periodical within the volume

2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

19

Pages from-to

1587-1605

UT code for WoS article

001111213800001

EID of the result in the Scopus database

2-s2.0-85178449362