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)

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

“…Despite the ecological and economic significance of coastal environments, monitoring efforts to identify PFAS in these regions are limited. …”

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)

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

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

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

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

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

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Cohesiveness changes across IDDSI levels and food categories. by Muxi Chen (9294270)

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Multiple linear regression analysis results. by Muxi Chen (9294270)

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

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Adhesiveness changes across different IDDSI levels and food categories. by Muxi Chen (9294270)

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Statistical analysis of adhesiveness, hardness, and cohesiveness across IDDSI levels. by Muxi Chen (9294270)

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

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