Showing 1,101 - 1,120 results of 21,342 for search '(( significant decrease decrease ) OR ( significantly improves decrease ))', query time: 0.45s Refine Results
  1. 1101

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  2. 1102

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  3. 1103

    STL Linear Combination Forecast Graph. by Xiangjuan Liu (618000)

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  4. 1104

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  5. 1105

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  6. 1106

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  7. 1107

    Figure of ablation experiment. by Xiangjuan Liu (618000)

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  8. 1108

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  9. 1109

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  10. 1110

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  11. 1111

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  12. 1112

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
  13. 1113

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

    Published 2025
    “…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”
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