بدائل البحث:
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
greater decrease » greatest decrease (توسيع البحث), greater increase (توسيع البحث), greater disease (توسيع البحث)
teer decrease » mean decrease (توسيع البحث)
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
greater decrease » greatest decrease (توسيع البحث), greater increase (توسيع البحث), greater disease (توسيع البحث)
teer decrease » mean decrease (توسيع البحث)
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3561
STL Linear Combination Forecast Graph.
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. …"
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3562
LOSS curves for BWO-BiLSTM model training.
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. …"
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3563
Analysis of STL-PCA prediction results.
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. …"
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3564
Accumulated contribution rate of PCA.
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. …"
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3565
Figure of ablation experiment.
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. …"
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3566
Flowchart of the STL-PCA-BWO-BiLSTM model.
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. …"
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3567
Parameter optimization results of BiLSTM.
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. …"
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3568
Descriptive statistical analysis of data.
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. …"
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3569
The MAE value of the model under raw data.
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. …"
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3570
Three error values under raw data.
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. …"
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3571
Decomposition of time scries plot.
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. …"
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3572
Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry
منشور في 2025"…In Ion Mobility Spectrometry (IMS), ion gates are essential for controlling ion flow, significantly impacting detection sensitivity and resolution. …"
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3573
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3574
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3575
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3576
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3577
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3578
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3579
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3580