北京富士通研发中心实习报告

邱 诚

报告主题富士通的工作

Auto-Regressive and Moving Average Model (ARMA)介绍

RHadoop介绍

富士通的工作

研究数据选择方式； TBSC 均值法 指示性片段

优化 ARMA模型和 SVR模型；动态结合 ARMA模型和 SVR模型；

均值法描述基本步骤

查找与预测天 1~9点的欧式距离最接近的五天； 将所得到的五天通过 10~20点的欧式距离进行展； 将前两步得到的全部天通过 k-means聚成两类； 挑选预测天之前最接近的同一工作日作为判定天，和两个聚类中心计算欧式距离，挑选距离较小的聚类；

将所得聚类中的各天求平均值作为预测结果。

ARMA 模型介绍

ARMA模型原理

ARMA模型优化

R 中 ARMA模型的使用

ARMA 基本原理Auto-Regressive model

Moving Average model

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ARMA 基本原理

自回归模型描述的是当前值与历史值之间的关系；

滑动平均模型描述的是自回归部分的误差累计；

ARMA模型就是通过将自回归模型的预测值与累计误差相结合；

ARMA 模型的优化

Akaike’s Information Criterion (AIC)

AIC, Bias Corrected (AICc)

Bayesian Information Criterion (BIC)

以上优化都是针对通过最大似然估计进行拟合得到的ARMA模型

AIC 优化指标

：代表最大似然；

：代表模型的参数个数；

R 中 ARMA 模型的使用

arima

auto.arima

arima 函数arima ( x,

order = c(0, 0, 0),

seasonal = list(order = c(0, 0, 0), period = NA),

xreg = NULL,

include.mean = TRUE,

transform.pars = TRUE,

fixed = NULL,

init = NULL,

method = c("CSS-ML", "ML", "CSS"),

n.cond,

optim.method = "BFGS",

optim.control = list(),

kappa = 1e6

)

R 中 arima 参数说明

auto.arima 函数auto.arima( x,

d=NA, D=NA, max.p=5, max.q=5, max.P=2, max.Q=2, max.order=5, start.p=2, start.q=2, start.P=1, start.Q=1, stationary=FALSE, ic=c("aicc","aic", "bic"), stepwise=TRUE, trace=FALSE, approximation=(length(x)>100 | frequency(x)>12), xreg=NULL, test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"), allowdrift=TRUE, lambda=NULL, parallel=FALSE, num.cores=NULL

)

Nowadays, we have lots of data. BIG DATA!

What is R?

What is R?

Why R?

Why R?

What need?

There is a need for more than counts and averages on these big data sets

Analyzing all of the data can lead to insights that sampling or subsets can’t reveal

Why R and Hadoop?

Why R and Hadoop?

Why R and Hadoop?

Why R and Hadoop?

RHadoop 介绍

Rhadoop 用途 The open-source RHadoop project makes it

easier to extract data from Hadoop for analysis with R, and to run R within the nodes of the Hadoop cluster -- essentially, to transform Hadoop into a massively-parallel

statistical computing cluster based on R.

Rhadoop

rhdfs

Manipulate HDFS directly from R

Mimic as much of the HDFS Java API as possible

rhdfs Functions

rmr

Designed to be the simplest and most elegant way to write MapReduce programs

Gives the R programmer the tools necessary to perform data analysis in a way that is “R” like

Provides an abstraction layer to hide the implementation details

rmr mapreduce Function

Thank you!