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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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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: 定义坐标轴的显示范围用axis指令
语法：axis([xmin xmax ymin ymax]) 你的例子ymin ymax应分别设为0和0.5，xmin xmax 取周期的两端。 然后再plot（x,y）
欢迎一起探讨matlab的使用：） -draw picture in matlab
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Size: 22528 |
Author: 周易 |
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Description: 在matlab的环境下实现了自回归移动平均模型（arima）-Matlab environment in the realization of the auto-regressive moving average model (arima)
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Size: 1024 |
Author: xiongtao |
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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: 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: 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: 该代码是MATLAB写成的m文件及GUI文件，附带实例数据，用于ARIMA预测模型的应用及对该模型的研究-The code is written in MATLAB m-files and GUI files, with the instance data, research and application of the model of ARIMA forecasting model for
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Size: 36864 |
Author: 肖胜强 |
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Description: 基于时间序列中通过arima和系统辨识工具箱在matlab上进行预测的实例。非周期性，可选择预测步数-Based on time sequence by arima and system identification toolbox in matlab to predict the instance. Aperiodic, can choose steps prediction
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Size: 1024 |
Author: 朱鸶羽 |
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Description: matlab中时间序列预测代码，包括ARIMA和季节性时间序列代码(Time series prediction code in MATLAB, including ARIMA and seasonal time series code)
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Size: 1024 |
Author: Zero灬
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Description: 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，是指将非平稳时间序列转化为平稳时间序列，然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归所建立的模型。ARIMA模型根据原序列是否平稳以及回归中所含部分的不同，包括移动平均过程（MA）、自回归过程（AR）、自回归移动平均过程（ARMA）以及ARIMA过程。(To address time consuming and parameter sensitivity in the emerging decomposition- ensemble models, this paper develops a non-iterative learning paradigm without iterative training process.)
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Size: 1024 |
Author: luc1fer
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