目的为了适应铸造CAE技术的网络化趋势,满足铸造CAE系统前置处理模块对STL模型高级渲染的功能性需求,开发一款足以媲美OpenGL渲染环境下复杂三维图形渲染效果的Web版的STL模型查看器程序——STLViewer。方法仿效Windows桌面程序的运行...目的为了适应铸造CAE技术的网络化趋势,满足铸造CAE系统前置处理模块对STL模型高级渲染的功能性需求,开发一款足以媲美OpenGL渲染环境下复杂三维图形渲染效果的Web版的STL模型查看器程序——STLViewer。方法仿效Windows桌面程序的运行方式和界面风格,选择单页面设计方案。选用Visual Studio 2019开发平台,利用HTML5、CSS3和JavaScript技术设计程序界面。深入研究基于WebGL的STL模型可视化技术,按照依托场景环境活动模型渲染的技术路线,进行STLViewer各功能模块的开发。结果设计并实现了STLViewer,该程序功能完整性良好、内部逻辑结构合理高效。STLViewer融隐式交互和显式交互于一体,具有本地STL模型的随机性访问、活动模型的多样化交互、模型姿态的智能化跟踪、视图动画的多方式呈现、模型导出的便捷化操作等特点,实现了网络环境下STL模型的高级渲染功能。结论STLViewer作为一款性能卓越的STL模型查看器程序,既可辅助用户制订合理的网格剖分方案,又能带来优良的用户体验,在实际应用中得到了良好效果。展开更多
校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average M...校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。展开更多
金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIM...金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.展开更多
随着对地观测技术的飞速发展,我们能够以前所未有的精度和频率获取地球表面的各种数据,通过进行更精细的空间分析和时间序列分析,可以揭示地理环境变化的深层次规律。本文旨在建立中国降水量变化趋势及其与海拔、坡度、土地利用之间的...随着对地观测技术的飞速发展,我们能够以前所未有的精度和频率获取地球表面的各种数据,通过进行更精细的空间分析和时间序列分析,可以揭示地理环境变化的深层次规律。本文旨在建立中国降水量变化趋势及其与海拔、坡度、土地利用之间的预测模型。通过对降水量、地形因素和五种主要土地覆盖类型的相关性分析,运用Logistic回归和随机森林模型探讨了这些因素对灾害发生的共同影响机制。此外,采用ARIMA时间序列模型预测了未来2025年到2035年间的降水量和土地利用格局,并结合随机森林模型评估了此期间各地区暴雨灾害风险的空间分布。研究结果揭示了在极端天气条件下最脆弱的地区,为灾害防范和土地规划提供了重要参考。With the rapid advancement of remote sensing technologies, we are now able to obtain various data on the Earth’s surface with unprecedented accuracy and frequency. Through more refined spatial and time series analyses, the underlying patterns of geographical environmental changes can be revealed. This study aims to establish predictive models for the trends in precipitation changes in China and their relationships with elevation, slope, and land use. By analyzing the correlations between precipitation, topographic factors, and five major land cover types, the study employs Logistic regression and Random Forest models to explore the joint impact of these factors on the occurrence of disasters. Additionally, the ARIMA time series model is utilized to forecast precipitation and land use patterns from 2025 to 2035, while the Random Forest model is applied to assess the spatial distribution of rainfall disaster risks during this period. The results of the study highlight the most vulnerable regions under extreme weather conditions, providing valuable insights for disaster prevention and land planning.展开更多
本文基于国家统计局官网关于纺织行业2018年01月~2023年12月的工业生产者出厂价格指数(PPI)数据,运用时间序列分析方法构建了ARIMA(1,2,1)模型,并对该模型进行了深入的拟合分析。利用所建立的模型对2024年全年12个月的PPI月度数据进行预...本文基于国家统计局官网关于纺织行业2018年01月~2023年12月的工业生产者出厂价格指数(PPI)数据,运用时间序列分析方法构建了ARIMA(1,2,1)模型,并对该模型进行了深入的拟合分析。利用所建立的模型对2024年全年12个月的PPI月度数据进行预测,并将预测结果与实际值进行对比,结果显示预测值的相对误差远低于5%,充分验证了模型具有良好的拟合度和预测精度。此外,在进行未来预测时,需要充分考虑外部因素的影响,并对模型进行适时的调整和优化,以提高预测的准确性和可靠性。Based on the data of the Producer Price Index (PPI) of the textile industry from January 2018 to December 2023 from the official website of the National Bureau of Statistics, this paper constructs the ARIMA(1,2,1) model using the time series analysis method, and conducts an in-depth fitting analysis of the model. By using the established model to predict the PPI monthly data for the whole year of 2024 for 12 months, and comparing the prediction results with the actual values in detail, the results show that the relative error of the predicted value is far less than 5%, which fully verifies that the model has good fit and prediction accuracy. In addition, when making future predictions, it is necessary to fully consider the influence of external factors and adjust and optimize the model in a timely manner to improve the accuracy and reliability of the prediction.展开更多
文摘目的为了适应铸造CAE技术的网络化趋势,满足铸造CAE系统前置处理模块对STL模型高级渲染的功能性需求,开发一款足以媲美OpenGL渲染环境下复杂三维图形渲染效果的Web版的STL模型查看器程序——STLViewer。方法仿效Windows桌面程序的运行方式和界面风格,选择单页面设计方案。选用Visual Studio 2019开发平台,利用HTML5、CSS3和JavaScript技术设计程序界面。深入研究基于WebGL的STL模型可视化技术,按照依托场景环境活动模型渲染的技术路线,进行STLViewer各功能模块的开发。结果设计并实现了STLViewer,该程序功能完整性良好、内部逻辑结构合理高效。STLViewer融隐式交互和显式交互于一体,具有本地STL模型的随机性访问、活动模型的多样化交互、模型姿态的智能化跟踪、视图动画的多方式呈现、模型导出的便捷化操作等特点,实现了网络环境下STL模型的高级渲染功能。结论STLViewer作为一款性能卓越的STL模型查看器程序,既可辅助用户制订合理的网格剖分方案,又能带来优良的用户体验,在实际应用中得到了良好效果。
文摘校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。
文摘金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.
文摘随着对地观测技术的飞速发展,我们能够以前所未有的精度和频率获取地球表面的各种数据,通过进行更精细的空间分析和时间序列分析,可以揭示地理环境变化的深层次规律。本文旨在建立中国降水量变化趋势及其与海拔、坡度、土地利用之间的预测模型。通过对降水量、地形因素和五种主要土地覆盖类型的相关性分析,运用Logistic回归和随机森林模型探讨了这些因素对灾害发生的共同影响机制。此外,采用ARIMA时间序列模型预测了未来2025年到2035年间的降水量和土地利用格局,并结合随机森林模型评估了此期间各地区暴雨灾害风险的空间分布。研究结果揭示了在极端天气条件下最脆弱的地区,为灾害防范和土地规划提供了重要参考。With the rapid advancement of remote sensing technologies, we are now able to obtain various data on the Earth’s surface with unprecedented accuracy and frequency. Through more refined spatial and time series analyses, the underlying patterns of geographical environmental changes can be revealed. This study aims to establish predictive models for the trends in precipitation changes in China and their relationships with elevation, slope, and land use. By analyzing the correlations between precipitation, topographic factors, and five major land cover types, the study employs Logistic regression and Random Forest models to explore the joint impact of these factors on the occurrence of disasters. Additionally, the ARIMA time series model is utilized to forecast precipitation and land use patterns from 2025 to 2035, while the Random Forest model is applied to assess the spatial distribution of rainfall disaster risks during this period. The results of the study highlight the most vulnerable regions under extreme weather conditions, providing valuable insights for disaster prevention and land planning.
文摘本文基于国家统计局官网关于纺织行业2018年01月~2023年12月的工业生产者出厂价格指数(PPI)数据,运用时间序列分析方法构建了ARIMA(1,2,1)模型,并对该模型进行了深入的拟合分析。利用所建立的模型对2024年全年12个月的PPI月度数据进行预测,并将预测结果与实际值进行对比,结果显示预测值的相对误差远低于5%,充分验证了模型具有良好的拟合度和预测精度。此外,在进行未来预测时,需要充分考虑外部因素的影响,并对模型进行适时的调整和优化,以提高预测的准确性和可靠性。Based on the data of the Producer Price Index (PPI) of the textile industry from January 2018 to December 2023 from the official website of the National Bureau of Statistics, this paper constructs the ARIMA(1,2,1) model using the time series analysis method, and conducts an in-depth fitting analysis of the model. By using the established model to predict the PPI monthly data for the whole year of 2024 for 12 months, and comparing the prediction results with the actual values in detail, the results show that the relative error of the predicted value is far less than 5%, which fully verifies that the model has good fit and prediction accuracy. In addition, when making future predictions, it is necessary to fully consider the influence of external factors and adjust and optimize the model in a timely manner to improve the accuracy and reliability of the prediction.
