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. …”

C2f-E improvement process. by Pingping Yan (462509)

“…Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Passive sensing data. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Surveys. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Regional tourism demand indicators and comprehensive evaluation. by Jilei Tao (20208621)

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Spatial Markov transfer matrix (<i>k</i> = 4). by Jilei Tao (20208621)

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Spatial pattern of supply-demand coupling coordination level. by Jilei Tao (20208621)

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Roadmap of the technical approach. by Jilei Tao (20208621)

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Parameters related to standard deviational ellipse of tourist attractions accessibility. by Jilei Tao (20208621)

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Tourist attractions accessibility of county-level administrative units. by Jilei Tao (20208621)

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Frequency of accessibility time distribution of tourist attractions at the grid scale. by Jilei Tao (20208621)

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Speed and time cost for different road types. by Jilei Tao (20208621)

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Scope of the study area. by Jilei Tao (20208621)

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Spatial distribution of accessibility to tourist attractions at the grid scale. by Jilei Tao (20208621)

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Time cost of natural environment. by Jilei Tao (20208621)

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Traditional Markov transfer matrix (<i>k</i> = 4). by Jilei Tao (20208621)

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Standard deviational ellipse of tourist attractions accessibility. by Jilei Tao (20208621)

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