يعرض 3,781 - 3,800 نتائج من 21,342 نتيجة بحث عن '(( significantly ((linked decrease) OR (linear decrease)) ) OR ( significant decrease decrease ))', وقت الاستعلام: 0.42s تنقيح النتائج
  1. 3781

    BWO-BiLSTM model prediction results. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  2. 3782

    Change in panel quantile regression coefficients. حسب Getachew Magnar Kitila (19935139)

    منشور في 2024
    "…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …"
  3. 3783

    Bi-LSTM architecture diagram. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  4. 3784

    LOSS curves for BWO-BiLSTM model training. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  5. 3785

    Definitions of variables and measurements. حسب Getachew Magnar Kitila (19935139)

    منشور في 2024
    "…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …"
  6. 3786

    Analysis of STL-PCA prediction results. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  7. 3787

    Accumulated contribution rate of PCA. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  8. 3788

    Regression estimates: Double threshold model. حسب Getachew Magnar Kitila (19935139)

    منشور في 2024
    "…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …"
  9. 3789

    Figure of ablation experiment. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  10. 3790

    Results from cross sectional dependence test. حسب Getachew Magnar Kitila (19935139)

    منشور في 2024
    "…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …"
  11. 3791

    Flowchart of the STL-PCA-BWO-BiLSTM model. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  12. 3792

    Parameter optimization results of BiLSTM. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  13. 3793

    Descriptive statistical analysis of data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  14. 3794

    The MAE value of the model under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  15. 3795

    Three error values under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  16. 3796

    Panel quantile regression results. حسب Getachew Magnar Kitila (19935139)

    منشور في 2024
    "…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …"
  17. 3797

    Decomposition of time scries plot. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. 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. …"
  18. 3798
  19. 3799
  20. 3800