{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T16:51:33Z","timestamp":1784652693896,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472144"],"award-info":[{"award-number":["62472144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["252400410396"],"award-info":[{"award-number":["252400410396"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024Z005"],"award-info":[{"award-number":["2024Z005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan Province Key R&amp;D and Promotion Special Project (Soft Science)","award":["62472144"],"award-info":[{"award-number":["62472144"]}]},{"name":"Henan Province Key R&amp;D and Promotion Special Project (Soft Science)","award":["252400410396"],"award-info":[{"award-number":["252400410396"]}]},{"name":"Henan Province Key R&amp;D and Promotion Special Project (Soft Science)","award":["2024Z005"],"award-info":[{"award-number":["2024Z005"]}]},{"name":"\u201cScience and Technology Innovation Yongjiang 2035\u201d Major Application Demonstration Plan Project in Ningbo","award":["62472144"],"award-info":[{"award-number":["62472144"]}]},{"name":"\u201cScience and Technology Innovation Yongjiang 2035\u201d Major Application Demonstration Plan Project in Ningbo","award":["252400410396"],"award-info":[{"award-number":["252400410396"]}]},{"name":"\u201cScience and Technology Innovation Yongjiang 2035\u201d Major Application Demonstration Plan Project in Ningbo","award":["2024Z005"],"award-info":[{"award-number":["2024Z005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning models often fail to accurately predict future trends in complex financial data due to inherent limitations. To address these challenges, this study introduces a WOA-BiLSTM-ARIMA hybrid forecasting model leveraging parameter optimization. Specifically, the whale optimization algorithm (WOA) optimizes hyperparameters for the Bidirectional Long Short-Term Memory (BiLSTM) network, overcoming parameter tuning challenges in conventional approaches. Due to its strong capacity for nonlinear feature extraction, BiLSTM excels at modeling nonlinear patterns in financial time series. To mitigate the shortcomings of BiLSTM in capturing linear patterns, the Autoregressive Integrated Moving Average (ARIMA) methodology is integrated. By exploiting ARIMA\u2019s strengths in modeling linear features, the model refines BiLSTM\u2019s prediction residuals, achieving more accurate and comprehensive financial time series forecasting. To validate the model\u2019s effectiveness, this paper applies it to the prediction experiment of future spread data. Compared to classical models, WOA-BiLSTM-ARIMA achieves significant improvements across multiple evaluation metrics. The mean squared error (MSE) is reduced by an average of 30.5%, the mean absolute error (MAE) by 20.8%, and the mean absolute percentage error (MAPE) by 29.7%.<\/jats:p>","DOI":"10.3390\/a18080517","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T14:24:28Z","timestamp":1755267868000},"page":"517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid BiLSTM-ARIMA Architecture with Whale-Driven Optimization for Financial Time Series Forecasting"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6457-5853","authenticated-orcid":false,"given":"Panke","family":"Qin","sequence":"first","affiliation":[{"name":"School of Software, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Software, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Li","sequence":"additional","affiliation":[{"name":"Ningbo Artificial Intelligence Institute, Shanghai Jiaotong University, Ningbo 315000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2114-3025","authenticated-orcid":false,"given":"Zhongqi","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Software, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlun","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0481-7317","authenticated-orcid":false,"given":"Haoran","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjie","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Software, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1007\/s11831-022-09765-0","article-title":"Review of ML and AutoML solutions to forecast time-series data","volume":"29","author":"Alsharef","year":"2022","journal-title":"Arch. 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