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Description: 使用时间序列进行短时交通预测的小程序-Using time series of short-term traffic forecasts applet
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Size: 1024 |
Author: 花海 |
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Description: Forecast Multiplicative ARIMA Model
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Size: 1024 |
Author: mojy |
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Description: arima model sequences
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Size: 10240 |
Author: dost |
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Description: The code performs the simulation of time series with autoregressive fractionally integrated moving average (ARFIMA) models that generalize ARIMA (autoregressive integrated moving average) and ARMA autoregressive moving average models. ARFIMA models allow non-integer values of the differencing parameter and are useful in modeling time series with long memory. The model is generally represented as RFIMA(p,d,q) model where d is the differencing parameter and p and q are the order of the autoregressive and moving average parts of the model respectively. -The code performs the simulation of time series with autoregressive fractionally integrated moving average (ARFIMA) models that generalize ARIMA (autoregressive integrated moving average) and ARMA autoregressive moving average models. ARFIMA models allow non-integer values of the differencing parameter and are useful in modeling time series with long memory. The model is generally represented as RFIMA(p,d,q) model where d is the differencing parameter and p and q are the order of the autoregressive and moving average parts of the model respectively.
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Size: 3072 |
Author: ammar_muayad |
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Description: 基于R语言的ARIMA模型构建和测试，用于初学者学习时间序列分析。-ARIMA model based on R programming language for time series analysis
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Size: 2048 |
Author: Harris |
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Description: 基于ARIMA时间序列模型对风速进行拟合，预测短时间的风速-Based on ARIMA time series model fitting for wind speed forecasting wind speed for a short period of time
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Size: 1024 |
Author: Damon |
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Description: example auto arima in r
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Size: 1024 |
Author: ekolondo |
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Description: 基于ARIMA方法对数据进行预测分析，判断出预测趋势-Based on ARIMA forecasting methods for data analysis to determine the predicted trend
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Size: 1024 |
Author: 刘生 |
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Description: 基于R语言的金融工程统计数据分析。包括：
1.SVR，ANNS等机器学习算法及编程；
2.ARIMA,FARIMA,GATCH等传统统计学时间序列知识点及编程；
3.风险管理相关知识以及编程；
4.数理金融相关知识以及编程等。-Statistical Analysis of Financial Engineering Based on R Language. include:
1.SVR, ANNS and other machine learning algorithms and programming
2.ARIMA, FARIMA, GATCH and other traditional statistical time series of knowledge points and programming
3. Knowledge of risk management and programming
4. Mathematical and financial knowledge and programming.
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Size: 11108352 |
Author: 张正聪 |
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Description: Arima算法的实现，matlab包含准备选取，方差值求解-Arima algorithm to achieve, matlab contains the preparation of the selection, the value of variance
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Size: 2048 |
Author: caoyu |
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Description: Artificial neural network to build人工神经网络建造-Artificial neural network to build
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Size: 2048 |
Author: 他里雾 |
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Description: 可以进行时许预测,预测个数可以自行修改，哈哈，可以跑出来的(predict time sequence)
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Size: 91136 |
Author: 妙世居士
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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.)
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Size: 2026496 |
Author: 艾玛菲尔
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Description: 采用ARIMA模型（自回归积分滑动平均模型）+三次指数平滑法(Holt-Winters)，用scala语言实现的在spark平台运行的分布式时间序列预测算法(Using the ARIMA model (autoregressive integral moving average model) + Holt-Winters (Holt-Winters), using scala language to achieve the spark platform to run the distributed time series prediction algorithm)
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Size: 33792 |
Author: 小龙女
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Description: Arima模型的一些函数，不全，仅供参考，m语言帮助文档里的，我自己也不怎么会，所以还请见谅(Incomplete, for reference only, M language help document, I do not know how, so please forgive me)
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Size: 571392 |
Author: kevencjd
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Description: 可以很好的预测数据，程序代码简单可靠好用(Good prediction of data, the program code is simple and reliable easy to use.)
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Size: 1024 |
Author: James Hardon
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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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Description: spark 实现的时间序列算法，包括ARIMA等(spark time-series,including ARIMA etc.)
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Size: 217088 |
Author: 578893415 |
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Description: 时间序列法，通过过去数据来建立相应模型来预测未来数据(Time series, using past data to establish corresponding models to predict future data)
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Size: 1024 |
Author: 暗自窃喜 |
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