Description: 样本自相关函数和偏相关函数，仅供大家参考，多多指教-Sample autocorrelation function and the partial correlation function, we only refer to the exhibitions Platform: |
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Author:戴海峰 |
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Description: 。在提取图像特征点的时候采用提取角点的方式，用角点检测与图像的局部自相关函数紧密结合的方法，通过角点分析来判断待检测点是否为角点。-In the extraction of image features: by the way, extracts angular point with corner detection and image of partial autocorrelation function of integrated method, through the corner to judge whether the line to analysis for the corner. Platform: |
Size: 1024 |
Author:sfm |
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Description: 任给一组数据，通过函数求解样本均值、样本自协方差函数、样本自相关函数和样本偏自相关函数-As to a set of data, through solving the sample mean value and function sample covariance function, sample from the autocorrelation function and sample partial autocorrelation function
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Size: 1024 |
Author:zf |
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Description: 径流预报常用的几种模型：AR模型，BP模型，RBF模型，GM(1,N)模型；预报数据预处理方法：自相关函数以及偏自相关函数确定法；EMD方法-Several commonly used runoff forecasting model: AR model, BP model, RBF model, GM (1, N) model forecast data preprocessing methods: autocorrelation function and partial autocorrelation function to determine the law EMD Method Platform: |
Size: 5120 |
Author:明天 |
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Description: ARIMA模型是通过将预测对象随时间推移而形成的数据序列当成一个随机序列，进而用一定的数学模型来近似表述该序列。根据原序列是否平稳以及回归中所包含部分的不同分为AR、MA、ARMA以及ARIMA过程。
在模型的使用过程中需要根据时间序列的自相关函数、偏自相关函数等对序列的平稳性进行判别；而对于非平稳序列一般都需要通过差分处理将其转换成平稳序列（ARIMA）；对得到的平稳序列进行建模以确定最佳模型（AR、MA、ARMA或者ARIMA）。在使用中最重要也是最关键的就是对序列进行参数估计，以检验其是否具有统计意义。(The ARIMA model uses a mathematical model to approximate the sequence of data by forming a sequence of data that is predicted over time. It is divided into AR, MA, ARMA and ARIMA processes according to the stability of the original sequence and the included part of the regression.
In the process of the model according to the autocorrelation function, the partial sequence of stationary sequence autocorrelation function of discrimination; and for non stationary sequences generally need treatment to convert it into stationary sequence by difference (ARIMA); for the stationary sequences obtained were modeled to determine the best model (AR ARMA, MA, or ARIMA). In use, the most important and most important is to estimate the parameters of the sequence to test whether it is statistically significant.) Platform: |
Size: 2026496 |
Author:艾玛菲尔
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Description: The code allows calculating the autocorrelation function and the partial autocorrelation function of a time series. The algorithm is based on the Schwartz selection criteria, also called the BIC criterion. Also, the code allows to project the time series. Platform: |
Size: 3150 |
Author:franciscososasotomayor123 |
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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) Platform: |
Size: 4077568 |
Author:fanny123 |
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Search - partial autocorrelation function