在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出...在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出力数据的目标函数,利用二阶泰勒展开式对目标函数进行近似处理。结合分位数回归构(Quantile Regression,QR)改进长短期记忆(Long Short Term Memory,LSTM)递归神经网络,构建QRLSTM模型将近似处理后的数据输入至该模型中,通过逻辑门完成新能源出力预测。在测试结果中,实际方法在不同环境条件下对于新能源机组出力情况的预测结果均与实际情况保持较高的拟合度,具有较高的精准度。展开更多
以2014—2019年珲春地区红外相机拍摄的东北虎数据为基础,基于XGBoost算法构建了虎出没区域风险等级划分模型。由模型检验可知:模型的准确率为93.51%,精确率为93.85%,召回率为93.08%,F1值为93.31%,Cohen s Kappa统计系数为90.2%。研究...以2014—2019年珲春地区红外相机拍摄的东北虎数据为基础,基于XGBoost算法构建了虎出没区域风险等级划分模型。由模型检验可知:模型的准确率为93.51%,精确率为93.85%,召回率为93.08%,F1值为93.31%,Cohen s Kappa统计系数为90.2%。研究结果表明:基于XGBoost算法构建的人-虎共存区域风险等级划分模型分类效果好、预测准确度高,运用该模型对人-虎共存区域进行风险等级划分是可行的。展开更多
Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been invol...Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.展开更多
文摘在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出力数据的目标函数,利用二阶泰勒展开式对目标函数进行近似处理。结合分位数回归构(Quantile Regression,QR)改进长短期记忆(Long Short Term Memory,LSTM)递归神经网络,构建QRLSTM模型将近似处理后的数据输入至该模型中,通过逻辑门完成新能源出力预测。在测试结果中,实际方法在不同环境条件下对于新能源机组出力情况的预测结果均与实际情况保持较高的拟合度,具有较高的精准度。
文摘以2014—2019年珲春地区红外相机拍摄的东北虎数据为基础,基于XGBoost算法构建了虎出没区域风险等级划分模型。由模型检验可知:模型的准确率为93.51%,精确率为93.85%,召回率为93.08%,F1值为93.31%,Cohen s Kappa统计系数为90.2%。研究结果表明:基于XGBoost算法构建的人-虎共存区域风险等级划分模型分类效果好、预测准确度高,运用该模型对人-虎共存区域进行风险等级划分是可行的。
文摘Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.
