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. …”

Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

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Autophagy significantly increases RGCs loss. by Hongmei Qian (538647)

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Variation of the most appropriate p value significance threshold with different ratios of the standard deviations and sample sizes. by Farrokh Habibzadeh (12256529)

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Impact on different areas of health. by Margherita Molaro (17803989)

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Summary of significance levels for comparison of surgical segment ROM between different test groups. by Jia Zhu (135506)

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Descriptive statistics of alcohol consumption in G7 countries (in litres/capita). by Luis Alberiko Gil-Alana (14659817)

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Data for the G7 countries in 2022. by Luis Alberiko Gil-Alana (14659817)

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Estimates of the differencing parameter, d, in Eq (1). by Luis Alberiko Gil-Alana (14659817)

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Estimates of the differencing parameter, d, in Eq (1). by Luis Alberiko Gil-Alana (14659817)

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Time series plots: Alcohol consumption. by Luis Alberiko Gil-Alana (14659817)

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Estimated coefficients based on the selected model in Table 4. by Luis Alberiko Gil-Alana (14659817)

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Estimated coefficients based on the selected model in Table 3. by Luis Alberiko Gil-Alana (14659817)

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The variables that significantly predicted litter clump weight in shelter cats. by Allison Andrukonis (11846404)