Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate mode...Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.展开更多
In this paper,we propose a novel study for gesture identification using surface electromyography(sEMG)signal,and the raw sEMG signal and the sEMG envelope signal are collected by the sensor at the same time.An efficie...In this paper,we propose a novel study for gesture identification using surface electromyography(sEMG)signal,and the raw sEMG signal and the sEMG envelope signal are collected by the sensor at the same time.An efficient method of gesture identification based on the combination of two signals using supervised learning and univariate feature selection is implemented.In previous research techniques,researchers tend to use the raw sEMG signal and extract several constant features for classification,which inevitably causes a result of ignoring individual differences.Our experiment shows that both the optimal feature set and redundant feature set are not same for different subjects.In order to address this problem,we extract all the common features from two signals,up to 76 features,most of which has been established as the common EMG-based gesture index.In addition,extracting too many features in an application can reduce operational efficiency,so we apply for feature selection to get the optimal feature set and decrease the number of extracting feature.As a result,the combination of two signals is better than using a single signal.The feature selection can be used to select optimal feature set from all features to achieve the best classification performance for each subject.The experimental results demonstrate that the proposed method achieves the performance with the highest accuracy of 95%for identifying up to nine gestures only using two sensors.Finally,we develop a real-time intelligent sEMG-driven bionic hand system by using the proposed method.展开更多
文摘Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.
文摘In this paper,we propose a novel study for gesture identification using surface electromyography(sEMG)signal,and the raw sEMG signal and the sEMG envelope signal are collected by the sensor at the same time.An efficient method of gesture identification based on the combination of two signals using supervised learning and univariate feature selection is implemented.In previous research techniques,researchers tend to use the raw sEMG signal and extract several constant features for classification,which inevitably causes a result of ignoring individual differences.Our experiment shows that both the optimal feature set and redundant feature set are not same for different subjects.In order to address this problem,we extract all the common features from two signals,up to 76 features,most of which has been established as the common EMG-based gesture index.In addition,extracting too many features in an application can reduce operational efficiency,so we apply for feature selection to get the optimal feature set and decrease the number of extracting feature.As a result,the combination of two signals is better than using a single signal.The feature selection can be used to select optimal feature set from all features to achieve the best classification performance for each subject.The experimental results demonstrate that the proposed method achieves the performance with the highest accuracy of 95%for identifying up to nine gestures only using two sensors.Finally,we develop a real-time intelligent sEMG-driven bionic hand system by using the proposed method.
文摘【目的】阅读理解是人类最重要的认知能力,评价人类的阅读理解能力需要客观指标。【方法】提出一种基于脑磁图(magnetoencephalogram, MEG)虚相干脑功能连接的预测模型,使用虚相干算法构建全脑MEG功能连接,并通过单变量特征选择算法对特征进行选择,采用偏最小二乘回归(Partial Least Squares, PLS)构建预测模型对阅读理解能力进行预测。【结果】基于MEG虚相干功能连接的偏最小二乘回归模型可以成功预测阅读理解分数;进行单变量特征选择的模型预测性能更高、预测更准确(R^(2)[PVT-Language]=0.524,MSE[PVT-Language]=5.042;R^(2)[ORRT-Language]=0.536,MSE[ORRT-Language]=5.142),并且发现采用与阅读理解相关的任务态数据集比静息态数据集更适合用来预测阅读理解能力,且特征选择的功能连接更精确。【结论】基于MEG虚相干功能连接的PLS预测模型可以用来客观评价人类阅读理解能力。
