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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

Figure of ablation experiment. حسب Xiangjuan Liu (618000)

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

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

"…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …"

Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. حسب Cristina Dolciotti (16317330)

الموضوعات:

Mean ± SD of the different parameters relative to bipedal stance on soft support with the eyes open recorded in the Chewing and Hand Grip groups. حسب Cristina Dolciotti (16317330)

الموضوعات:

Time course of Path Length, LFS and Velocity during postural training. حسب Cristina Dolciotti (16317330)

الموضوعات:

Relation of the changes in Y Sway Amplitude and LFS induced by postural training with those in 95% Area. حسب Cristina Dolciotti (16317330)

الموضوعات:

Training-induced changes in bipedal stance. حسب Cristina Dolciotti (16317330)

الموضوعات:

Between-group differences in 95% Area, Y Sway Amplitude and LFS during postural training. حسب Cristina Dolciotti (16317330)

الموضوعات:

Relation between training-related changes in unipedal and bipedal stance. حسب Cristina Dolciotti (16317330)

الموضوعات:

Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. حسب Cristina Dolciotti (16317330)

الموضوعات:

Consolidation of training-induced changes in unipedal stance: Path Length, X Sway Amplitude and Velocity. حسب Cristina Dolciotti (16317330)

الموضوعات:

Time course of 95% Area, X and Y Amplitude Sway during postural training. حسب Cristina Dolciotti (16317330)

الموضوعات:

Correlations between unipedal and bipedal stance parameters recorded at the beginning of the training and of the control session. حسب Cristina Dolciotti (16317330)

الموضوعات:

Flow diagram. حسب Cristina Dolciotti (16317330)

الموضوعات: