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At the core of this process is selecting a forecasting strategy; however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose\n                    <jats:italic>Stratify<\/jats:italic>\n                    , a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate\n                    <jats:italic>Stratify<\/jats:italic>\n                    on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80) in the univariate setting. In over 84% of 1080 experiments, novel strategies in\n                    <jats:bold>\n                      <jats:italic>Stratify<\/jats:italic>\n                    <\/jats:bold>\n                    improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners to explore the\n                    <jats:italic>Stratify<\/jats:italic>\n                    space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We share the code (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/zs18656\/stratify_unifying_MSF\" ext-link-type=\"uri\">https:\/\/github.com\/zs18656\/stratify_unifying_MSF<\/jats:ext-link>\n                    ) to reproduce our results.\n                  <\/jats:p>","DOI":"10.1007\/s10618-025-01135-1","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:09:07Z","timestamp":1754474947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Stratify: unifying multi-step forecasting strategies"],"prefix":"10.1007","volume":"39","author":[{"given":"Riku","family":"Green","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahraa S.","family":"Abdallah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Telmo M.","family":"Silva Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"1135_CR1","doi-asserted-by":"publisher","unstructured":"An, NH, Anh DT (2015) Comparison of strategies for multi-step-ahead prediction of time series using neural network. 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