搜索资源列表
arima
- arima时间序列预测源代码
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
doarima
- ARIMA模型,用来对非平稳信号的功率谱估计,很有用!-ARIMA model, used for non-stationary signals in power spectrum estimation, very useful!
arimapred
- ARIMA matlab实现 , 对时间序列进行预测分析-ARIMA matlab realize, prediction of time series analysis
ARIMA
- 自回归移动平均模型(Autoregressive Integrated Moving Average Model)的Matlab实现,时间序列分析代码-Autoregressive moving average model (Autoregressive Integrated Moving Average Model) to achieve the Matlab
arima_matlab
- 可用於arima的計算,檢測數據以及預測用途-do arima
1.52inch_initial_code
- ST7637初始化代码,主要应用在LG的手机上及MP4上.-ST7637 initital code Arima 1.52 CSTN
Model_ARIMA1
- 季节性移动自回归模型 可以进行时间序列的预测 尤其是季节性数据-S-Arima seaonal Arima model in matlab
arimatimeseries
- arima time series forecasting
ARIMA_model
- 基于MATLAB的ARIMA模型的源代码。ARIMA模型是自回归滑动平均求和模型,是时间序列分析模型,可以用于时间序列的预测。该代码实现了ARIMA模型的建模和谱分析过程-The ARIMA model based on MATLAB source code. ARIMA model is the sum of autoregressive moving average model is time series analysis mod
ARFIMA_SIM
- Generates a sample for an arima model
matlab
- matlab实现arima算法,这个代码里面有一个循环过程来选择合适的阶-arima algorithe
cpp
- This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally
Matlab
- ARIMA prediction in matlab. Just store inputs in y, and estimate parameters.
ARIMA
- 时间序列预测ARIMA模型,这是一种基于风速数据的预测程序。-ARIMA time series forecasting model, which is a program based on the forecast wind speed data.
chap7-The-ARIMA-Procedure
- 时间序列的 The ARIMA Procedure-The ARIMA Procedure
ARIMA
- autoregressive integrated moving average (ARIMA) time series java GUI using Netbeans IDE
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, aut
ARIMA
- ARIMA 模型是在平稳的时间序列基础上建立起来的,因此时间序列的平稳性是建模的重要前提。检验时间序列模型平稳的方法一般采用 ADF 单位根检验模型去检验。当然如果时间序列不稳定,也可以通过一些操作去使得时间序列稳定(比如取对数,差分),然后进行 ARIMA 模型预测,得到稳定的时间序列的预测结果,然后对预测结果进行之前使序列稳定的操作的逆操作(取指数,差分的逆操作),就可以得到原始数据的预测结果。(time series predic