搜索资源列表
随机过程 时间序列的分析
- 是随机过程中时间序列分析作业，基于Matlab编程实现模型判别，参数求取，模型预测等，另附有本人作业全文，供大家参考。-random process is time-series analysis of operations, based on the Matlab programming model checker, parameters strike, model projections, followed by my work, for your reference.
AOLMM
- 基于局域法多步预报模型的混沌时间序列预报模型，对多个典型混沌序列的仿真测试表明，本算法具有良好的多步预测精度和较好的抗噪声能力-based multi-step prediction model of chaotic time series prediction model, a number of typical chaotic sequence of simulation tests show that the algorithm has a good multi-step forecast
sunspot1
- 太阳黑子模型，采用时间序列，最小二乘法拟和，BIC检验，效果奇佳-sunspot model, time series, and the least squares method to be, BIC inspection work wonders
AR(5)
- 利用AR模型进行时间序列预测的程序源代码，使用最小二乘估计法进行参数估计。拟合效果非常好。-use AR model for time series prediction of the source code, the use of least squares estimation method to estimate parameters. Fitting very good results.
arimalik
- 用对数似然估计法求解时间序列分析中的ARIMA模型的matlab源代码-with several likelihood estimation method Time Series Analysis of the ARIMA model Matlab source code
tsa244
- 时间序列工具箱 ，内含双谱，AR模型参数估计程序-time series toolbox containing Bispectrum, AR model parameter estimation procedures
linear_system_identification.tar
- The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is
ARMA
- ARMA时间序列模型，matlab源程序，有参考价值-ARMA time series model, matlab source, a reference value
GM(1-1)-model-
- GM(1,1)残差预测模型,用于预测指数或对数规律的时间序列-GM (1,1) residual prediction model for predicting the time series of exponential or logarithmic laws
ARIMAtest
- 对于时间序列模型建立ARMA预测模型，可对未来值进行预报。(For the time series model, the ARMA prediction model is established, and the future value can be forecasted.)
基于Matlab的ARMA模型时间序列分析法仿真
- 对ARMA时间序列模型在matlab上进行仿真实现。(The ARMA time series model is simulated on matlab.)
算法大全
- 回归分析，神经网络讲解及代码，时间序列模型分析及代码，多元分析代码及讲解、偏最小二乘代码及讲解、(Regression analysis, neural network explanation and code, time series model analysis and code, multiple analysis code and explanation, partial least square code and explanation,)
算法大全
- 回归分析，神经网络讲解及代码，时间序列模型分析及代码，多元分析代码及讲解、偏最小二乘代码及讲解、(Regression analysis, neural network explanation and code, time series model analysis and code, multiple analysis code and explanation, partial least square code and explanation,)
gompeta
- 这个曲线是一种常用的时间序列模型 前面写变量 后边写要预测的几个数值就OK 非常方便 欢迎下载(This curve is a commonly used time series model. It is very convenient to write some variables to be predicted.)
Factor_Models
- 动态因子模型，该模型可以有效对高维数据进行降维，将成百上千的数据信息浓缩在几个因子里面，即从一国许多经济时间序列数据中估计和解释驱动各变量波动的共同动态因子。 MATLAB代码(dynamic factor model, the model can be effective for high-dimensional data dimension reduction, condensed the hundreds of thousands of data in several factors,
GRU
- 通过Python基于keras平台实现了时间序列模型gru(Realization of time series model Gru based on keras platform by Python)
ICSS
- 时间序列模型icss算法找到方差结构突变点得到变点的位置和个数(The time series model icss algorithm finds the position and number of the change point of the variance structure mutation point.)
LOLIMOT-master
- work, I have implemented a neuro-fuzzy neural network using Locally Linear Model Tree learning algorithm in order to predict chaotic time-series.
arima
- arima - （平稳性检验）根据时间序列的散点图、自相关系数和偏自相关系数、单位根检验(ADF)，来判断数据的平稳性； - （平稳化处理）对非平稳的时间序列数据进行差分处理，得到差分阶数d； - （白噪声检测）为了验证序列中有用的信息是否已被提取完毕，如果为白噪声序列，(arima arima -(Stableness test) According to the time series of scatter plots, autocorrelation coefficients and
Matlab-AR模型
- 时间序列分析(Time-Series Analysis)是指将原来的销售分解为四部分来看——趋势、周期、时期和不稳定因素， 然后综合这些因素， 提出销售预测。强调的是通过对一个区域进行一定时间段内的连续遥感观测，提取图像有关特征，并分析其变化过程与发展规模。(Time-Series Analysis model)