Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a gre...Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.展开更多
Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
为了解决传统栖息地预测模型中无法捕捉具有时间序列信息的环境因子对金枪鱼空间分布滞后影响的不足。采用2021—2024年金枪鱼围网渔捞日志数据,通过构建滞后天数为1、5、10、15 d的长短期记忆(Long-short term memory,LSTM)神经网络模...为了解决传统栖息地预测模型中无法捕捉具有时间序列信息的环境因子对金枪鱼空间分布滞后影响的不足。采用2021—2024年金枪鱼围网渔捞日志数据,通过构建滞后天数为1、5、10、15 d的长短期记忆(Long-short term memory,LSTM)神经网络模型,分别对单位捕捞努力量渔获量(Catch per unit of effort,CPUE)和经纬度进行了预测。研究表明,滞后10 d的模型精度最高,其均方误差(Mean square error,MSE)为0.018 7,平均绝对误差(Mean absolute error,MAE)为0.077 6,表明鲣空间分布受过去短期内环境累计效应的影响。通过对最佳模型进行验证,结果表明预测纬度与实际纬度之间的R2为0.97,预测经度与实际经度之间的R2为0.65,说明空间分布预测范围与实际基本吻合。为揭示鲣栖息地特征及其生态过程的动态机制提供了新的理解,同时为中西太平洋鲣围网渔业的科学管理提供了重要参考依据。展开更多
交通运输业减排是实现全局减排目标的关键。研究基于改进的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型分析影响交通运输业碳排放的主要因素,设置低碳、基准和高碳3种...交通运输业减排是实现全局减排目标的关键。研究基于改进的随机性环境影响评估(Stochastic Impacts by Regression on Population,Affluence,and Technology,STIRPAT)模型分析影响交通运输业碳排放的主要因素,设置低碳、基准和高碳3种情景方案,利用卷积神经网络-长短期记忆网络-注意力机制(Convolutional Neural Networks-Long short-Term Memory-Attention Mec.hanism,CNN-LSTM-Attention)交通运输业碳排放预测模型对中国30个省、自治区、直辖市2022—2035年交通运输业碳排放进行预测。结果显示:人口情况、经济水平和交通运输等3个维度的影响因素对交通运输业碳排放具有正向驱动作用,能源技术维度的影响因素则起负向驱动作用;CNN-LSTM-Attention交通运输业碳排放预测模型提升了模型在小样本数据集的预测能力,预测效果较好;低碳、基准和高碳3种情景下中国交通运输业的碳排放峰值将晚于2030年的总排放峰值目标实现;各省在碳排放峰值和达峰时间上存在异质性,应采取差异化、精准化的政策策略,局部上分区域、分梯次达峰,以整体上实现碳达峰目标。展开更多
文摘Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
文摘为了解决传统栖息地预测模型中无法捕捉具有时间序列信息的环境因子对金枪鱼空间分布滞后影响的不足。采用2021—2024年金枪鱼围网渔捞日志数据,通过构建滞后天数为1、5、10、15 d的长短期记忆(Long-short term memory,LSTM)神经网络模型,分别对单位捕捞努力量渔获量(Catch per unit of effort,CPUE)和经纬度进行了预测。研究表明,滞后10 d的模型精度最高,其均方误差(Mean square error,MSE)为0.018 7,平均绝对误差(Mean absolute error,MAE)为0.077 6,表明鲣空间分布受过去短期内环境累计效应的影响。通过对最佳模型进行验证,结果表明预测纬度与实际纬度之间的R2为0.97,预测经度与实际经度之间的R2为0.65,说明空间分布预测范围与实际基本吻合。为揭示鲣栖息地特征及其生态过程的动态机制提供了新的理解,同时为中西太平洋鲣围网渔业的科学管理提供了重要参考依据。
