Aiming at the problem of difficulties in modeling the nonlinear relation in the steam coal dataset, this article proposes a forecasting method for the price of steam coal based on robust regularized kernel regression and empirical mode decomposition. By selecting the polynomial kernel function, the robust loss function and L2 regular term to construct a robust regularized kernel regression model are used. The polynomial kernel function does not depend on the kernel parameters and can mine the global rules in the dataset so that improves the forecasting stability of the kernel model. This method maps the features to the high-dimensional space by using the polynomial kernel function to transform the nonlinear law in the original feature space into linear law in the high-dimensional space and helps learn the linear law in the high-dimensional feature space by using the linear model. The Huber loss function is selected to reduce the influence of abnormal noise in the dataset on the model performance, and the L2 regular term is used to reduce the risk of model overfitting. We use the combined model based on empirical mode decomposition (EMD) and auto regressive integrated moving average (ARIMA) model to compensate for the error of robust regularized kernel regression model, thus making up for the limitations of the single forecasting model. Finally, we use the steam coal dataset to verify the proposed model and such model has an optimal evaluation index value compared to other contrast models after the model performance is evaluated as per the evaluation index such as RMSE, MAE, and mean absolute percentage error.
Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.
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