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Description: 这是arma模型进行图像处理的一个文章，讲的非常详细，希望有所帮助-Arma model this is an image processing articles, very detailed talk about the hope that help
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Author: 杨海宁 |
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Description: ARMA 过程编程实现，不是很难，可以实现一步预测。-ARMA process of programming is not difficult, step in the forecast can be achieved.
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Size: 301056 |
Author: sada |
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Description: 时间序列ARMA预测股指，并进行相关性检验-Time Series ARMA forecast the stock index
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Author: d307718 |
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Description: 采用小波和ARMA模型对时间序列进行预测-Wavelet and the ARMA model to forecast the time series
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Size: 4096 |
Author: 李枫 |
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Description: 1、时间序列的ARMA（自回归移动平均）算法代码实现；
2、能用于平稳时序的分析和预测；
3、使用C/C++开发。-1、The codes relization of timeseries arma forecast method
2、Be used in analyzing and forecast in timeseries
3、Be developed by C/C++ tool.
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Size: 1024 |
Author: 刘亚东 |
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Description: matlab金融时间序列ARMA建模
结果分析：
1.预测结果从第四步开始，预测值不再改变，因为ARMA是收敛的回归模型，而我们做的工作并不是模拟，所以，当预测步长足够长时，它最终将收敛于一个不变得预测值
2.既然预测值一样，为什么还原为成交量后，在置信区间下预测的最大值与预测均值的差比预测均值与最小值的差要大？因为将对数差分值还原时，需用到的指数函数为凹函数-matlab Financial Time Series the the ARMA modeling results Analysis: 1. predicted results from the fourth step, the predicted values no longer change, because the the ARMA convergence regression model, and the work we do is not analog, so when the prediction step enough after a long time, it will eventually converge to a become predictive value since the predicted value, why restore volume, maximum predicted mean forecast in the confidence interval for the difference than forecast average with the minimum difference big? Because the logarithmic differential value reduction required to exponential function is a concave function
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Size: 92160 |
Author: wupeng |
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Description: 利用matlab软件建立模型，利用ARMA模型对数据进行预测-Using matlab software to model the data using the ARMA model prediction
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Size: 2048 |
Author: 大大大 |
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Description: Forecast analysis of time series data, ARMA algorithm using ARMA algorithm to implement this function
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Size: 9216 |
Author: 李继勇 |
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Description: 基于时间序列对用水量的预测，建立arma模型确定阶数和参数，然后通过前几年的数据和周期性的趋势对未来的数据进行预测和检验.包含实例源数据及所有代码。-Based on time sequence for the forecast of water consumption, the establishment of arma model order and parameters, and then through the data of a few years ago and periodic trend forecast and inspection of the data for the future
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Size: 3072 |
Author: 朱鸶羽 |
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Description: 用ARMA模型对时间序列进行预测，预测效果相对于移动平均预测较好，能进行有效预测。-Using the ARMA model to forecast the time sequence, better prediction effect relative to the moving average forecast, can effectively forecast.
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Size: 1024 |
Author: 凌远夏 |
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Description: 1、时间序列的ARMA（自回归移动平均）算法代码实现；2、能用于平稳时序的分析和预测；3、使用C/C++开发。-1、The codes relization of timeseries arma forecast method 2、Be used in analyzing and forecast in timeseries 3、Be developed by C/C++ tool.
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Size: 1024 |
Author: fu8164064 |
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Description: 时间序列分析的MATLAB程序，使用AIC准则确定最优ARMA(P,Q)模型，输出预测数据。-MATLAB program time series analysis, the use of AIC criterion to determine the optimal ARMA (P, Q) model output forecast data.
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Size: 2048 |
Author: 于金波 |
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Description: 房价指数预测HPI，用时间序列ARMA模型预测房价-HPI forecast
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Size: 2048 |
Author: 陆宏凯 |
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Description: 用于研究时间序列的方法有AR（自回归）、MA（滑动平均）、ARMA（自回归滑动平均）这三种模型。而对于一个平稳时间序列预测问题,首先要考虑的是寻求与它拟合最好的预测模型。而模型的识别与阶数的确定则是选择模型的关键。
1.用 迭代生成1000个点（前2个点自定义）。
2.在这1000个点中取800点进行时间序列分析建立合适的模型。
3.利用剩余的200个点进行模型预测，并看其是否匹配，最后校正。
-Methods for studying time series are AR (autoregressive), MA (moving average), ARMA (autoregressive moving average). For a stationary time series prediction problem, the first thing to consider is to find the best prediction model with it. And the identification of the model and the order of the selection is the key to the selection model.
1. Use iteration to generate 1000 points (the first two points to customize).
2. Take the 800 points in the 1000 points for time series analysis to establish the appropriate model.
3. Use the remaining 200 points to model the forecast and see if it matches and finalize it.
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Size: 13312 |
Author: 曹红英 |
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Description: ARMA 模型（Auto-Regressive and Moving Average Model）是研究时间序列的重要方法，由自回归模型（简称AR模型）与滑动平均模型（简称MA模型）为基础“混合”构成。在市场研究中常用于长期追踪资料的研究，如：Panel研究中，用于消费行为模式变迁研究；在零售研究中，用于具有季节变动特征的销售量、市场规模的预测等。(ARMA model is an important method for studying time series. It is composed of an autoregressive model (AR model) and a moving average model (MA model). In the market research is often used to study long-term tracking data such as: Panel, used to study the mode change in consumer behavior; retail research, for with the seasonal variation characteristics of the sales volume, market size forecast.)
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Author: 阿斯顿难题
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Description: ARMA 模型（Auto-Regressive and Moving Average Model）是研究时间序列的重要方法，由自回归模型（简称AR模型）与滑动平均模型（简称MA模型）为基础“混合”构成。在市场研究中常用于长期追踪资料的研究，如：Panel研究中，用于消费行为模式变迁研究；在零售研究中，用于具有季节变动特征的销售量、市场规模的预测等(ARMA model is an important method to study time series. It consists of auto regressive model (AR model) and moving average model (MA model). In the market research is often used to study long-term tracking data such as: Panel, used to study the mode change in consumer behavior; retail research, for with the seasonal variation characteristics of the sales volume, market size forecast)
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Size: 1024 |
Author: dovemeng
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Description: 时间序列预测(time forecast)
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Size: 496640 |
Author: 索隆1 |
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Description: 从网页抓取大盘数据并用ARMA模型进行拟合和预测(Grab data from web pages and use ARMA model to fit and predict.)
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Size: 1024 |
Author: Sam sky |
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