Search alternatives:
market decrease » marked decrease (Expand Search), largest decrease (Expand Search), marked increase (Expand Search)
values decrease » values increased (Expand Search), largest decrease (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
a market » _ market (Expand Search), a marker (Expand Search)
i values » _ values (Expand Search)
market decrease » marked decrease (Expand Search), largest decrease (Expand Search), marked increase (Expand Search)
values decrease » values increased (Expand Search), largest decrease (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
a market » _ market (Expand Search), a marker (Expand Search)
i values » _ values (Expand Search)
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The MAE value of the model under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Three error values under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Comparison of environmental perception time results at different learning rates.
Published 2025Subjects: -
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The multiple criteria qualitative value-based pricing framework “MARIE” for new clinical status
Published 2025“…However, the failure to incorporate a drug’s value when determining its price decreases pharmaceutical companies’ motivation to develop and launch novel drugs in Japan.…”
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Estimation and prediction of the incidence <i>I</i>(<i>t</i>) in synthetic data.
Published 2024“…For each of these scenarios, we consider four testing and reporting models (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012687#sec009" target="_blank">Methods</a> for details): constant reporting exponent <i>n</i> = −1 and testing that grows indefinitely with incidence (left column); constant reporting exponent <i>n</i> = −2 and testing that grows indefinitely with incidence (center-left column); variable reporting exponent <i>n</i> = −<i>α</i> log <i>C</i> and testing that grows indefinitely with incidence (center-right column); variable reporting exponent <i>n</i> = −<i>α</i> log <i>C</i> and testing that saturates at high incidence values (right column). …”
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