Search alternatives:
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
improve decrease » improved urease (Expand Search), improves disease (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
improve decrease » improved urease (Expand Search), improves disease (Expand Search)
-
961
Accumulated contribution rate of PCA.
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. …”
-
962
Figure of ablation experiment.
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. …”
-
963
Flowchart of the STL-PCA-BWO-BiLSTM model.
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. …”
-
964
Parameter optimization results of BiLSTM.
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. …”
-
965
Descriptive statistical analysis of data.
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. …”
-
966
The MAE value of the model under raw data.
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. …”
-
967
Three error values under raw data.
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. …”
-
968
Decomposition of time scries plot.
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. …”
-
969
-
970
-
971
-
972
-
973
-
974
-
975
-
976
-
977
Survival function analysis of mortality for Viral load status and Treatment Interruption status.
Published 2025Subjects: -
978
-
979
-
980
Survival function analysis of treatment Interruption for Viral load status and BMI status.
Published 2025Subjects: