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

“…<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. …”

Figure of ablation experiment. by Xiangjuan Liu (618000)

“…<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. …”

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

“…<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. …”

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

“…<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. …”

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

“…<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. …”

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

“…<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. …”

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

“…<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. …”

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

“…<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. …”

Schematic presentation <i>M</i>. <i>bovis</i> PG45 5′ nucleotidase and phylogenetic tree of its homologs. by Aga E. Gelgie (17661720)

Subjects:

Baseline patient characteristics. by Oscar F. C. van den Bosch (22184246)

Subjects:

Effects of s-ketamine and midazolam on respiratory parameters. by Oscar F. C. van den Bosch (22184246)

Subjects:

Flow chart of the study participants. by Oscar F. C. van den Bosch (22184246)

Subjects:

Assessing Bivalves as Biomonitors of Per- and Polyfluoroalkyl Substances in Coastal Environments by Shannon E. Jones (17558652)

Subjects:

S1 File - Global burden and risk factors of peptic ulcer disease between 1990 and 2021: An analysis from the global burden of disease study 2021 by Wende Hao (10315597)

Subjects:

The table shows the significant and non-significant altered group with different parameters. by Ashraf Attia Mahmoud (21262201)

Subjects:

Comparison of the cohesion ranges of different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects:

Relationships between cohesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Hardness increase across different IDDSI levels and food categories. by Muxi Chen (9294270)

Subjects:

Relationships between adhesiveness and IDDSI levels. by Muxi Chen (9294270)

Subjects:

Comparison of adhesiveness ranges for different food categories under IDDSI levels. by Muxi Chen (9294270)

Subjects: