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

Ahmed Bouteska Orcid Logo, Marco Lavazza Seranto, Petr Hajek, Mohammad Abedin

Annals of Operations Research

Swansea University Author: Mohammad Abedin

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DOI (Published version): 10.1007/s10479-023-05722-7

Abstract

Recent trends in global climate modeling, coupled with the availability of more fine-scaledatasets, have opened up opportunities for deep learning-based climate prediction to improvethe accuracy of predictions over traditional physics-based models. For this, however, largeensembles of data are neede...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65186
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spelling v2 65186 2023-12-02 Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks 4ed8c020eae0c9bec4f5d9495d86d415 Mohammad Abedin Mohammad Abedin true false 2023-12-02 CBAE Recent trends in global climate modeling, coupled with the availability of more fine-scaledatasets, have opened up opportunities for deep learning-based climate prediction to improvethe accuracy of predictions over traditional physics-based models. For this, however, largeensembles of data are needed. Generative models have recently proven to be a suitablesolution to this problem. For a sound generative model for time-series forecasting, it isessential that temporal dynamics are preserved in that the generated data obey the originaldata distributions over time. Existing forecasting methods aided by generative models arenot adequate for capturing such temporal relationships. Recently, generative models havebeen proposed that generate realistic time-series data by exploiting the combinations ofunsupervised and supervised learning. However, these models suffer from instable learningand mode collapse problems. To overcome these issues, here we propose Wasserstein TimeSeries Generative Adversarial Network (WTGAN), a new forecasting model that effectivelyimitates the dynamics of the original data by generating realistic synthetic time-series data. Tovalidate the proposed forecasting model, we evaluate it by backtesting the challenging decadalclimate forecasting problem. We show that the proposed forecasting model outperformsstate-of-the- art generative models. Another advantage of the proposed model is that onceWTGAN is tuned, generating time-series data is very fast, whereas standard simulators. Journal Article Annals of Operations Research 0 Springer Science and Business Media LLC 0254-5330 1572-9338 Forecasting, climate, deep learning, time series 1 12 2023 2023-12-01 10.1007/s10479-023-05722-7 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-10-02T14:42:06.1545040 2023-12-02T10:36:19.5942091 School of Management Accounting and Finance Ahmed Bouteska 0000-0002-5710-501x 1 Marco Lavazza Seranto 2 Petr Hajek 3 Mohammad Abedin 4 65186__29840__c7b5f52ad79a44eeb9b696c27161091a.pdf 65186.VOR.pdf 2024-03-25T15:19:10.7917287 Output 2054640 application/pdf Version of Record true This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. true eng http://creativecommons.org/licenses/by/4.0/
title Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
spellingShingle Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
Mohammad Abedin
title_short Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
title_full Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
title_fullStr Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
title_full_unstemmed Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
title_sort Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Ahmed Bouteska
Marco Lavazza Seranto
Petr Hajek
Mohammad Abedin
format Journal article
container_title Annals of Operations Research
container_volume 0
publishDate 2023
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-023-05722-7
publisher Springer Science and Business Media LLC
college_str School of Management
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hierarchy_top_id schoolofmanagement
hierarchy_top_title School of Management
hierarchy_parent_id schoolofmanagement
hierarchy_parent_title School of Management
department_str Accounting and Finance{{{_:::_}}}School of Management{{{_:::_}}}Accounting and Finance
document_store_str 1
active_str 0
description Recent trends in global climate modeling, coupled with the availability of more fine-scaledatasets, have opened up opportunities for deep learning-based climate prediction to improvethe accuracy of predictions over traditional physics-based models. For this, however, largeensembles of data are needed. Generative models have recently proven to be a suitablesolution to this problem. For a sound generative model for time-series forecasting, it isessential that temporal dynamics are preserved in that the generated data obey the originaldata distributions over time. Existing forecasting methods aided by generative models arenot adequate for capturing such temporal relationships. Recently, generative models havebeen proposed that generate realistic time-series data by exploiting the combinations ofunsupervised and supervised learning. However, these models suffer from instable learningand mode collapse problems. To overcome these issues, here we propose Wasserstein TimeSeries Generative Adversarial Network (WTGAN), a new forecasting model that effectivelyimitates the dynamics of the original data by generating realistic synthetic time-series data. Tovalidate the proposed forecasting model, we evaluate it by backtesting the challenging decadalclimate forecasting problem. We show that the proposed forecasting model outperformsstate-of-the- art generative models. Another advantage of the proposed model is that onceWTGAN is tuned, generating time-series data is very fast, whereas standard simulators.
published_date 2023-12-01T14:42:04Z
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score 11.037166

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