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Description: arima时间序列预测源代码
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Size: 7696 |
Author: cpmcgrady |
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Description: 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
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Size: 1024 |
Author: 温泉 |
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Description: 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!
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Size: 2048 |
Author: yaoshuyou |
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Description: ＡＲＩＭＡ matlab实现 ， 对时间序列进行预测分析-ARIMA matlab realize, prediction of time series analysis
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Size: 1024 |
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Description:
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Size: 2048 |
Author: 花花 |
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Description: 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
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Size: 8192 |
Author: Peter |
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Description: 可用於arima的計算,檢測數據以及預測用途-do arima
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Size: 7168 |
Author: 翁子翔 |
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Description: 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
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Size: 45056 |
Author: sunnichou |
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Description: 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
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Size: 1024 |
Author: 曾晨 |
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Description: arima time series forecasting
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Size: 118784 |
Author: sudek |
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Description: 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 models, can be used for time series prediction. The code to achieve the ARIMA models and spectral analysis of the modeling process
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Size: 2048 |
Author: 王二 |
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Description: Generates a sample for an arima model
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Size: 3072 |
Author: walid |
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Description: 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
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Size: 1024 |
Author: zhaoxing |
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Description: 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 distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests – a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the
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Size: 28672 |
Author: sakthivel |
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Description: ARIMA prediction in matlab.
Just store inputs in y, and estimate parameters.
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Size: 2048 |
Author: bastian |
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Description: 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.
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Size: 4096 |
Author: 马峰 |
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Description: 时间序列的 The ARIMA Procedure-The ARIMA Procedure
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Size: 497664 |
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Description: autoregressive integrated moving average (ARIMA) time series java GUI using Netbeans IDE
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Size: 1716224 |
Author: sigit |
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Description: 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 partial autocorrelation coefficients, unit root test (ADF), to determine the stability of the data;
-(Model identification and ordering) Establish a corresponding time series model based on the identified features. After the smoothing process, if the partial autocorrelation function is censored, and the autocorrelation function is tailed, an AR model is established; if the partial autocorrelation function is tailed, and the autocorrelation function is truncated, it is established MA model; if both the partial autocorrelation function and the autocorrelation function are trailing, the sequence is suitable for the ARIMA model. You can use the BIC criterion to order the model and)
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Size: 4077568 |
Author: fanny123 |
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Description: 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 prediction ARIMA)
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Size: 1024 |
Author: 西南角 |
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