Question

从R blog跟进，这对于使用Weibull参数模拟未知区域的时间序列非常有用且非常有用。

虽然这种方法给出了一个相当好的时间序列估计，但是当我们寻找季节性变化时，它会受到很大的影响。为了解释季节性变化，我想采用季节性最大风速并进行时间序列合成，使年度分布保持不变，即。形状和尺度参数（年度值）。

我希望通过使用12种不同的最大风速（每个月每个风速）对下面的代码使用季节性最大风速。这样可以在某个月允许更大的风速，在其他月份允许更低的风速，甚至可以使得到的时间序列更均匀。

代码如下：

MeanSpeed<-7.29 ## Mean Yearly Wind Speed at the site.

Shape=2; ## Input Shape parameter (yearly).
Scale=8 ##Calculated Scale Parameter ( yearly).

MaxSpeed<-17 (##yearly)
## $$$ 12 values of these wind speed one for each month to be used. The resultant time series should satisfy shape and scale parameters $$ ###
nStates<-16 

nRows<-nStates;
nColumns<-nStates;


LCateg<-MaxSpeed/nStates; 


WindSpeed=seq(LCateg/2,MaxSpeed-LCateg/2,by=LCateg) ## Fine the velocity vector-centered on the average value of each category.

##Determine Weibull Probability Distribution.
wpdWind<-dweibull(WindSpeed,shape=Shape, scale=Scale); # Freqency distribution.

plot(wpdWind,type = "b", ylab= "frequency", xlab = "Wind Speed")  ##Plot weibull probability distribution.

norm_wpdWind<-wpdWind/sum(wpdWind); ## Convert weibull/Gaussian distribution to normal distribution.

## Correlation between states (Matrix G)
g<-function(x){2^(-abs(x))} ## decreasing correlation function between states.
G<-matrix(nrow=nRows,ncol=nColumns)
G <- row(G)-col(G)
G <- g(G)

##--------------------------------------------------------


## iterative process to calculate the matrix P (initial probability)
P0<-diag(norm_wpdWind);   ## Initial value of the MATRIX P.
P1<-norm_wpdWind;  ## Initial value of the VECTOR p.


## This iterative calculation must be done until a certain error is exceeded
## Now, as something tentative, I set the number of iterations

steps=1000;  
P=P0; 
p=P1; 

for (i in 1:steps){
    r<-P%*%G%*%p;
    r<-as.vector(r/sum(r)); ## The above result is in matrix form. I change it to vector
    p=p+0.5*(P1-r)
    P=diag(p)}

   ## $$ ----Markov Transition Matrix --- $$ ##

N=diag(1/as.vector(p%*%G));## normalization matrix

MTM=N%*%G%*%P ## Markov Transition Matrix

MTMcum<-t(apply(MTM,1,cumsum));## From the MTM generated the accumulated

##-------------------------------------------
## Calculating the series from the MTMcum

##Insert number of data sets. 
LSerie<-52560; Wind Speed every 10 minutes for a year. 

RandNum1<-runif(LSerie);## Random number to choose between states
State<-InitialState<-1;## assumes that the initial state is 1 (this must be changed when concatenating days)
StatesSeries=InitialState;

## Initallise----

## The next state is selected to the one in which the random number exceeds the accumulated probability value
##The next iterative procedure chooses the next state whose random number is greater than the cumulated probability defined by the MTM
for (i in 2:LSerie) {
  ## i has to start on 2 !!
    State=min(which(RandNum1[i]<=MTMcum[State,]));

    ## if (is.infinite (State)) {State = 1}; ## when the above condition is not met max -Inf
    StatesSeries=c(StatesSeries,State)}

RandNum2<-runif(LSerie); ## Random number to choose between speeds within a state

SpeedSeries=WindSpeed[StatesSeries]-0.5+RandNum2*LCateg;
##where the 0.5 correction is needed since the the WindSpeed vector is centered around the mean value of each category.


print(fitdistr(SpeedSeries, 'weibull')) ##MLE fitting of SpeedSeries

有人可以建议我需要对代码进行哪些更改以及哪些更改？

Answer 1

我对生成风速时间序列知之甚少，但这些指南可能会帮助您提高代码可读性/可重用性：

＃1 您可能希望拥有一个可以产生风速时间的功能系列给出了一些观测值和季节性最大风速。因此，首先尝试在块中定义代码，如下所示：

wind_time_serie <- function(nobs, max_speed){
    #some code here
}

＃2 这样做，如果您的代码的某些部分似乎对生成风速时间序列有用，但与风速时间序列无关，请尝试将它们放入函数中（例如您计算的部分norm_wpdWind，您计算的部分MTMcum，...）。

＃3 然后，当您的define全局变量应该消失并成为函数中的默认参数时，代码中的部分开头。

＃4 当您的行已经很长时，请避免使用结束注释，并删除结束的分号。

#This
  State<-InitialState<-1;## assumes that the initial state is 1 (this must be changed when concatenating days)

#Would become this:
  #Assumes that the initial state is 1 (this must be changed when concatenating days)
  State<-InitialState<-1

然后您的代码应该更容易被其他人重用/读取。下面是一个应用于rnorm部分的指南的示例：

  norm_distrib<-function(maxSpeed, states = 16, shape = 2, scale = 8){

    #Fine the velocity vector-centered on the average value of each category.
    LCateg<-maxSpeed/states
    WindSpeed=seq(LCateg/2,maxSpeed-LCateg/2,by=LCateg) 

    #Determine Weibull Probability Distribution.
    wpdWind<-dweibull(WindSpeed,shape=shape, scale=scale)

    #Convert weibull/Gaussian distribution to normal distribution.
    return(wpdWind/sum(wpdWind))

  }

  #Plot normal distribution with the max speed you want (e.g. 17)
  plot(norm_distrib(17),type = "b", ylab= "frequency", xlab = "Wind Speed")

为风速时间序列添加季节性变化

1 个答案: