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    Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

    Published 2024-10-01
    “…For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the reduction in Mean Absolute Percentage Error (MAPE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with ML models without probability informing. …”
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    IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

    Published 2025-07-01
    “…Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>32.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> across various subsets under diverse solar conditions. …”
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