搜索资源列表
ARMA
- ARMA 模型（Auto-Regressive and Moving Average Model）是研究时间序列的重要方法，由自回归模型（简称AR模型）与滑动平均模型（简称MA模型）为基础“混合”构成。在市场研究中常用于长期追踪资料的研究，如：Panel研究中，用于消费行为模式变迁研究；在零售研究中，用于具有季节变动特征的销售量、市场规模的预测等。(ARMA model is an important method for studying time series. It is composed
Rayleigh_fading_channel_simulation
- This program is to simulate the Rayleigh fading channels using a p-th order autoregressive model AR(p)
speech reconstruction+SLP
- This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show t
xknk
- An AutoRegressive Moving Average Spectral Analysis toolbox f
自回归模型课件与程序
- 自回归模型，向量自回归模型是AR模型的推广。[1] 这个概念应当区别于金融风险管理的VaR模型。VaR模型是用于衡量市场风险和信用风险的大小，辅助金融机构进行风险管理和监管部门有效监管的工具(Autoregressive model and vector autoregressive model are the extension of AR model)
自回归模型课件与程序
- 自回归模型课件与程序,j机器学习；人工智能(Autoregressive model courseware and program, j machine learning)
spark-timeSeries
- 采用ARIMA模型（自回归积分滑动平均模型）+三次指数平滑法(Holt-Winters)，用scala语言实现的在spark平台运行的分布式时间序列预测算法(Using the ARIMA model (autoregressive integral moving average model) + Holt-Winters (Holt-Winters), using scala language to achieve the spark platform to run the distribut
sart_g
- 针对空间统计学空间自回归Tobit模型进行贝叶斯估计(Bayesian estimation of spatial autoregressive Tobit model for spatial statistics)
sar
- 对于空间统计学中空间自回归进行操作的代码(Code for spatial autoregressive operations in spatial statistics)
AR
- 基于现行自回归预测模型的MATLAB代码，通过历史数据预测当前数据并实时修正当前权重参数值(Based on the MATLAB code of the current autoregressive prediction model, the current data is predicted by historical data and the current weight parameter values are corrected in real time)
yuce
- 预测未来想要的数据，自己修改xo，自回归预测(Predict future data, modify XO, autoregressive prediction)
matlab程序
- 卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全及包含噪声的测量中，估计动态系统的状态。(Calman filtering is an efficient recursive filter (autoregressive filter). It can estimate the state of dynamic system from a series of incomplete and noisy measurements.)
arimanet
- ARIMA模型全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)，是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法[1] ，所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA（p，d，q）称为差分自回归移动平均模型，AR是自回归， p为自回归项； MA为移动平均，q为移动平均项数，d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型，是指将非平稳
AR
- AR自回归线性滤波器法模拟风速时程曲线，MATLAB(AR autoregressive linear filter method is used to simulate wind speed time history curve, MATLAB)
depth_recovery_AR
- Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model 这篇论文的源代码(source code of Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model)
tvar1
- 非平稳信号分析与处理,可用于特征提取，将AR模型扩展应用于非平稳时间序列，得到具有时变系数的时变自回归(time-varying autoregressive, TVAR)模型。(nonstationary random signal analysis and processing)
BP2
- 该程序为bp自回归预测，主要针对光伏/风电/水文等自回归(BP autoregressive prediction)
20558
- Autoregressive conditional kurtosis
KF
- 卡尔曼滤波器是一个“optimal recursive data processing algorithm（最优化自回归数据处理算法）C++实现编程(Calman filter is a "optimal recursive data processing algorithm" (optimized autoregressive data processing algorithm) C++ implementation programming.)
Untitled3
- 对输入信号进行AR自回归计算，计算出AR模型谱估计的值，对之后的计算提供帮助。(AR autoregressive calculation for input signal)