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
Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the l...Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements.This study investigates the predictive capabilities of two distinct methodologies—Long Short-Term Memory(LSTM)networks and Autoregressive Integrated Moving Average(ARIMA)models—using Apple Inc.(AAPL)stock price data spanning 2016 to 2024.By synthesizing theoretical frameworks with empirical analysis,the research evaluates how each model captures linear trends and nonlinear fluctuations,ultimately proposing a hybrid ARIMA-LSTM architecture to enhance forecasting accuracy.Finally,according to the principal characteristics of the two models,the ARIMA-LSTM hybrid model is constructed.The results show that the hybrid model significantly outperforms single models in terms of RMSE and directional accuracy.Combined with error distribution visualization and volatility analysis,the hybrid model demonstrates efficient performance in achieving prediction optimization through the decomposition of linear and nonlinear components.It provides a new methodological perspective for financial time series modeling.展开更多
在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异...在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。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
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.
文摘Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements.This study investigates the predictive capabilities of two distinct methodologies—Long Short-Term Memory(LSTM)networks and Autoregressive Integrated Moving Average(ARIMA)models—using Apple Inc.(AAPL)stock price data spanning 2016 to 2024.By synthesizing theoretical frameworks with empirical analysis,the research evaluates how each model captures linear trends and nonlinear fluctuations,ultimately proposing a hybrid ARIMA-LSTM architecture to enhance forecasting accuracy.Finally,according to the principal characteristics of the two models,the ARIMA-LSTM hybrid model is constructed.The results show that the hybrid model significantly outperforms single models in terms of RMSE and directional accuracy.Combined with error distribution visualization and volatility analysis,the hybrid model demonstrates efficient performance in achieving prediction optimization through the decomposition of linear and nonlinear components.It provides a new methodological perspective for financial time series modeling.
文摘在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。
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。
