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The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value. 相似文献
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Kadir Sentosun Marta N. Sanz Ortiz K. Joost Batenburg Luis M. Liz‐Marzán Sara Bals 《Particle & Particle Systems Characterization》2015,32(12):1063-1067
Characterization of core–shell type nanoparticles in 3D by transmission electron microscopy (TEM) can be very challenging. Especially when both heavy and light elements coexist within the same nanostructure, artifacts in the 3D reconstruction are often present. A representative example would be a particle comprising an anisotropic metallic (Au) nanoparticle coated with a (mesoporous) silica shell. To obtain a reliable 3D characterization of such an object, a dose‐efficient strategy is proposed to simultaneously acquire high‐angle annular dark‐field scanning TEM and annular dark‐field tilt series for tomography. The 3D reconstruction is further improved by applying an advanced masking and interpolation approach to the acquired data. This new methodology enables us to obtain high‐quality reconstructions from which also quantitative information can be extracted. This approach is broadly applicable to investigate hybrid core–shell materials. 相似文献
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研究了一种输出双音信号、低相位噪声、低杂散的频率合成方法.该方法首先利用锁相环路分别产生两路信号,并通过优化设计环路滤波器改善输出信号相位噪声,进而利用设计的Wilkinson功率合成器将两路信号进行功率合成,并通过衰减和放大来控制双音信号功率.基于本方法研制实现的输出双音频率为2 015和2 020 MHz的频率合成器,输出功率范围-12~18 dBm,且连续可调,输出信号相位噪声优于-93 dBc/Hz@1 kHz,在输出功率4 dBm以下时,双音互调成分低于-50 dBc,可用于各种测试系统频率源,尤其便于对非线性系统的测试. 相似文献
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《无线电通信技术》2019,(4):441-444
针对V/U频段,提出了一种宽带发射机的前端设计方法,其中包括射频信道设计、频率合成器的设计以及基带电路设计。鉴于小型化的设计思想,射频前端采用零中频发射机架构,通过合理的滤波、放大等信号处理,输出1 dB压缩点为16 dBm。频率合成器采用集成VCO的锁相环芯片ADF4351,为满足高速跳频通信体制,采用兵乓环高速换频,实现单边带相位噪声优于-125 dBc/Hz@100 kHz,频率切换时间小于10μs。基带电路采用正交调制器LTC5598和12位双通道DA转换器AD9716,可实现最大带宽为40 MHz的I/Q双路同时调制。通过FPGA或DSP基带处理,可广泛用于V/U小型化发射机设计。 相似文献
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近红外漫反射光谱法测定玉米秸秆NDF与ADF含量 总被引:21,自引:5,他引:16
应用主成分空间和傅里叶变换近红外光谱技术,采用偏最小二乘回归法(PLS),在国内首次建立了适合不同品种类型、不同生长发育时期和不同部位且适配范围广的近红外漫反射光谱(NIRS)测定玉米秸秆中性洗涤纤维(NeutralDetergentFiber,NDF)和酸性洗涤纤维(AcidDetergentFiber,ADF)含量的稳定校正模型。结果表明,采用一阶导数 矢量归一化预处理和一阶导数 多元散射校正预处理,谱区均为7502~5450cm-1和4601~4247cm-1,所建立的NDF与ADF校正模型,其校正和预测效果最佳。其校正决定系数(R2cal)均大于094,交叉验证和外部验证决定系数(R2cv,R2val)为092~096,各项误差(RMSEE,RMSECV和RMSEP)为149%~181%。该结果对青贮玉米秸秆材料快速鉴定和筛选具有重要的意义。 相似文献