Commodity markets,such as crude oil and precious metals,play a strategic role in the economic development of nations,with crude oil prices influencing geopolitical relations and the global economy.Moreover,gold and si...Commodity markets,such as crude oil and precious metals,play a strategic role in the economic development of nations,with crude oil prices influencing geopolitical relations and the global economy.Moreover,gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides.Therefore,accurate forecasting of crude oil and precious metals prices is critical.Nevertheless,due to the nonlinear nature,substantial fluctuations,and irregular cycles of crude oil and precious metals,predicting their prices is a challenging task.Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models.We address this gap by including silver alongside gold in our analysis,offering a more comprehensive understanding of the precious metal markets.This research expands existing knowledge and provides valuable insights into predicting commodity prices.In this study,we implemented 16 deep-and machine-learning models to forecast the daily price of the West Texas Intermediate(WTI),Brent,gold,and silver markets.The employed deep-learning models are long short-term memory(LSTM),BiLSTM,gated recurrent unit(GRU),bidirectional gated recurrent units(BiGRU),T2V-BiLSTM,T2V-BiGRU,convolutional neural networks(CNN),CNN-BiLSTM,CNN-BiGRU,temporal convolutional network(TCN),TCN-BiLSTM,and TCN-BiGRU.We compared the forecasting performance of deep-learning models with the baseline random forest,LightGBM,support vector regression,and k-nearest neighborhood models using mean absolute error(MAE),mean absolute percentage error,and root mean squared error as evaluation criteria.By considering different sliding window lengths,we examine the forecasting performance of our models.Our results reveal that the TCN model outperforms the others for WTI,Brent,and silver,achieving the lowest MAE values of 1.444,1.295,and 0.346,respectively.The BiGRU model performs best for gold,with an MAE of 15.188 using a 30-day input sequence.Furthermore,LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall.These findings are critical for investors,policymakers,mining companies,and governmental agencies to effectively anticipate market trends,mitigate risk,manage uncertainty,and make timely decisions and strategies regarding crude oil,gold,and silver markets.展开更多
电池储能系统(battery energy storage systems,BESSs)的假数据注入攻击(false data injection attacks,FDIAs)可以篡改传感器采集的电池测量信息,影响BESSs的荷电状态(state of charge,SOC)估计,从而威胁BESSs的安全与稳定运行。针对...电池储能系统(battery energy storage systems,BESSs)的假数据注入攻击(false data injection attacks,FDIAs)可以篡改传感器采集的电池测量信息,影响BESSs的荷电状态(state of charge,SOC)估计,从而威胁BESSs的安全与稳定运行。针对电池储能系统SOC估计的FDIAs,搭建了电池等效电路模型,利用扩展卡尔曼滤波(extended kalman filter,EKF)算法进行SOC估计,构造了不同强度的FDIAs,并提出一种基于T2V-Transformer(Time2Vector-Transformer)的FDIAs智能化检测方法。考虑到Transformer位置编码不能提取序列数据的时间特征,所以采用Time2Vector将时间转换为嵌入向量,提取电池数据的时间顺序特征,捕获序列周期性和非周期性特征。实验结果表明,与当前主流的长短期记忆网络(long short term memory,LSTM)自动编码器、Transformer等方法相比,所提方法可以检测出不同强度的电池储能系统FDIAs,并且在用时接近的情况下,具有更高的检测准确率。展开更多
文摘Commodity markets,such as crude oil and precious metals,play a strategic role in the economic development of nations,with crude oil prices influencing geopolitical relations and the global economy.Moreover,gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides.Therefore,accurate forecasting of crude oil and precious metals prices is critical.Nevertheless,due to the nonlinear nature,substantial fluctuations,and irregular cycles of crude oil and precious metals,predicting their prices is a challenging task.Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models.We address this gap by including silver alongside gold in our analysis,offering a more comprehensive understanding of the precious metal markets.This research expands existing knowledge and provides valuable insights into predicting commodity prices.In this study,we implemented 16 deep-and machine-learning models to forecast the daily price of the West Texas Intermediate(WTI),Brent,gold,and silver markets.The employed deep-learning models are long short-term memory(LSTM),BiLSTM,gated recurrent unit(GRU),bidirectional gated recurrent units(BiGRU),T2V-BiLSTM,T2V-BiGRU,convolutional neural networks(CNN),CNN-BiLSTM,CNN-BiGRU,temporal convolutional network(TCN),TCN-BiLSTM,and TCN-BiGRU.We compared the forecasting performance of deep-learning models with the baseline random forest,LightGBM,support vector regression,and k-nearest neighborhood models using mean absolute error(MAE),mean absolute percentage error,and root mean squared error as evaluation criteria.By considering different sliding window lengths,we examine the forecasting performance of our models.Our results reveal that the TCN model outperforms the others for WTI,Brent,and silver,achieving the lowest MAE values of 1.444,1.295,and 0.346,respectively.The BiGRU model performs best for gold,with an MAE of 15.188 using a 30-day input sequence.Furthermore,LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall.These findings are critical for investors,policymakers,mining companies,and governmental agencies to effectively anticipate market trends,mitigate risk,manage uncertainty,and make timely decisions and strategies regarding crude oil,gold,and silver markets.
文摘电池储能系统(battery energy storage systems,BESSs)的假数据注入攻击(false data injection attacks,FDIAs)可以篡改传感器采集的电池测量信息,影响BESSs的荷电状态(state of charge,SOC)估计,从而威胁BESSs的安全与稳定运行。针对电池储能系统SOC估计的FDIAs,搭建了电池等效电路模型,利用扩展卡尔曼滤波(extended kalman filter,EKF)算法进行SOC估计,构造了不同强度的FDIAs,并提出一种基于T2V-Transformer(Time2Vector-Transformer)的FDIAs智能化检测方法。考虑到Transformer位置编码不能提取序列数据的时间特征,所以采用Time2Vector将时间转换为嵌入向量,提取电池数据的时间顺序特征,捕获序列周期性和非周期性特征。实验结果表明,与当前主流的长短期记忆网络(long short term memory,LSTM)自动编码器、Transformer等方法相比,所提方法可以检测出不同强度的电池储能系统FDIAs,并且在用时接近的情况下,具有更高的检测准确率。
