本文的研究基于对浦东金桥股票1799个交易日的时间序列数据进行分析,旨在利用ARIMA-LSTM组合模型对该股票的收盘价进行精确预测。通过网格调参确定了ARIMA模型的最佳阶数,以确保模型能够有效捕捉时间序列数据中的趋势和周期性变化。利...本文的研究基于对浦东金桥股票1799个交易日的时间序列数据进行分析,旨在利用ARIMA-LSTM组合模型对该股票的收盘价进行精确预测。通过网格调参确定了ARIMA模型的最佳阶数,以确保模型能够有效捕捉时间序列数据中的趋势和周期性变化。利用这一优化后的ARIMA模型对浦东金桥的收盘价进行预测,从中获得预测残差。而后将残差数据输入到LSTM (长短期记忆网络)模型中。LSTM作为一种适合处理序列数据的深度学习模型,能够更好地捕捉数据中的长期依赖关系和非线性动态。通过结合ARIMA模型的残差和LSTM模型的预测能力,构建了一个ARIMA-LSTM组合模型,进一步提升了对浦东金桥收盘价未来走势的预测准确性和稳定性。This study is based on the analysis of 1799 trading days’ time series data of Pudong Jinqiao stock, aiming to use the ARIMA-LSTM combination model to accurately predict the closing price of the stock. The optimal order of the ARIMA model was determined through grid tuning to ensure that the model can effectively capture trends and periodic changes in time series data. Use this optimized ARIMA model to predict the closing price of Pudong Jinqiao and obtain the prediction residual. Then, the residual data will be inputted into the LSTM (Long Short Term Memory Network) model. LSTM, as a deep learning model suitable for processing sequential data, can better capture long-term dependencies and nonlinear dynamics in the data. By combining the residual of the ARIMA model with the predictive ability of the LSTM model, an ARIMA-LSTM combination model was constructed to further improve the accuracy and stability of predicting the future trend of the closing price of Pudong Jinqiao.展开更多
提出一种基于Real Sense技术的足部参数测量方法,仪器装置携带方便、成本低廉、操作简洁;该算法运用Intel Real Sense技术,搭建了使用多台SR300的足型重建系统;该系统首先将从深度图像中获取的各个足面轮廓点云在系统规定世界坐标系内...提出一种基于Real Sense技术的足部参数测量方法,仪器装置携带方便、成本低廉、操作简洁;该算法运用Intel Real Sense技术,搭建了使用多台SR300的足型重建系统;该系统首先将从深度图像中获取的各个足面轮廓点云在系统规定世界坐标系内叠加融合,然后用ICP算法进行精准配准完成对点云的融合,最后得到完整足部轮廓点云并根据定义计算足部围度等足部系列参数;在进行重建的过程中运用纹理贴图技术进行渲染,得到3D图像;实验表明,该系统能够快速精确地完成三维脚型恢复和足部特征参数的提取,具有很好的鲁棒性。展开更多
文摘本文的研究基于对浦东金桥股票1799个交易日的时间序列数据进行分析,旨在利用ARIMA-LSTM组合模型对该股票的收盘价进行精确预测。通过网格调参确定了ARIMA模型的最佳阶数,以确保模型能够有效捕捉时间序列数据中的趋势和周期性变化。利用这一优化后的ARIMA模型对浦东金桥的收盘价进行预测,从中获得预测残差。而后将残差数据输入到LSTM (长短期记忆网络)模型中。LSTM作为一种适合处理序列数据的深度学习模型,能够更好地捕捉数据中的长期依赖关系和非线性动态。通过结合ARIMA模型的残差和LSTM模型的预测能力,构建了一个ARIMA-LSTM组合模型,进一步提升了对浦东金桥收盘价未来走势的预测准确性和稳定性。This study is based on the analysis of 1799 trading days’ time series data of Pudong Jinqiao stock, aiming to use the ARIMA-LSTM combination model to accurately predict the closing price of the stock. The optimal order of the ARIMA model was determined through grid tuning to ensure that the model can effectively capture trends and periodic changes in time series data. Use this optimized ARIMA model to predict the closing price of Pudong Jinqiao and obtain the prediction residual. Then, the residual data will be inputted into the LSTM (Long Short Term Memory Network) model. LSTM, as a deep learning model suitable for processing sequential data, can better capture long-term dependencies and nonlinear dynamics in the data. By combining the residual of the ARIMA model with the predictive ability of the LSTM model, an ARIMA-LSTM combination model was constructed to further improve the accuracy and stability of predicting the future trend of the closing price of Pudong Jinqiao.
文摘提出一种基于Real Sense技术的足部参数测量方法,仪器装置携带方便、成本低廉、操作简洁;该算法运用Intel Real Sense技术,搭建了使用多台SR300的足型重建系统;该系统首先将从深度图像中获取的各个足面轮廓点云在系统规定世界坐标系内叠加融合,然后用ICP算法进行精准配准完成对点云的融合,最后得到完整足部轮廓点云并根据定义计算足部围度等足部系列参数;在进行重建的过程中运用纹理贴图技术进行渲染,得到3D图像;实验表明,该系统能够快速精确地完成三维脚型恢复和足部特征参数的提取,具有很好的鲁棒性。
