similar to: Rolling window linear regression

Dear Colleagues, I have faced with the problem that function rollaply with rolling window for calculation of volatility doesn't give the all results of calculations. I have run the rolling window for calculation in Excel and obtained that the number of outputs for Excel is 36 and for R is 18. The total number of observations is 37. In the attachment you can find pdf of the Excel and Excel

Hi mailinglist members, I’m actually working on a time series prediction and my current approach is to decompose the series first into a trend, a seasonal component and a remainder. Therefore I’m using the stl() function. But I’m wondering how to get the single components in order to predict the particular fitted series’. This code snippet illustrates my problem: series <-

Hi again, I am wondering there is any function for 'zoo' time series, where I can apply a user defined function rolling window basis, wherein window size is ever increasing i.e. not fixed. For example, let say I have below user defined function and a zoo time series : > library(zoo) > UDF = function(x) sum(x) > TS = zoo(rnorm(10), seq(as.Date('2017-01-01'),

Hello there, I am trying to compute the 3 months return momentum with the timeSeries x.ts, which is just a subset of simple returns from a much bigger series, > class(x.ts) [1] "timeSeries" attr(,"package") [1] "timeSeries" > dim(x.ts) [1] 20 3 > x.ts[1:8,] GMT MS.US AAPL.US CA.FP 1996-01-31 0.15159065 -0.133391894

Hello: I want to get a rolling estimation of the stdev of my data. Searching the document, I found the function "rollapply" in the zoo package. For example, my series is "c", and i want get a period of 10 days, so i write the command below: roll.sd = rollapply( c, 10, sd, na.pad = TRUE, align = 'right' ) but there is an error in it ,and the computing cannot be

Hi Joshua, thanks for your prompt reply. However as I said, sum() function I used here just for demonstrating the problem, I have other custom function to implement, not necessarily sum() I am looking for a generic solution for above problem. Any better idea? Thanks, On Fri, Aug 11, 2017 at 12:04 AM, Joshua Ulrich <josh.m.ulrich at gmail.com> wrote: > Use a `width` of integer index

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Use a `width` of integer index locations. And you likely want = "right" (or rollapplyr(), as I used). R> set.seed(21) R> x <- rnorm(10) R> rs <- rollapplyr(x, seq_along(x), sum) R> cs <- cumsum(x) R> identical(rs, cs) [1] TRUE On Thu, Aug 10, 2017 at 1:28 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi again, > > I am

Hello, I'm new to R and would like to know how to create a vector of "rolling sums". (I have seen the Rmetrics package and the rollMean function and I would like to do the same thing except Sum instead of Mean.) I imagine someone has done this, I just can't find it anywhere. Example: x <- somevector #where x is 'n' entries long #what I would like to do is: x1

Replace "sum" with your custom function's name. I don't see any reason why that wouldn't work, and the problem with my solution is not clear in your response. r <- rollapplyr(x, seq_along(x), yourCustomFunctionGoesHere) On Thu, Aug 10, 2017 at 1:39 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi Joshua, thanks for your prompt reply. However

Hope someone can help me here. I have a daily time series, say 2003-02-01 2003-02-03 2003-02-07 2003-02-09 2003-02-14 .......... 2004-02-01 2004-02-04 0.4914798 -1.1857653 -1.6982844 -0.3559572 -0.2333087 ........... 0.44553 -0.45222 I need to calculate the statistics for the overlapping rolling yearly window with rolling step as 1 day so for each of the intervals: (2003-02-01 ~

hi guys, I am using trivariate VAR model to get 10 step ahead orthogonalized impulse response functions. I want to use rolling sample analysis on the coefficients of the irf but I have no idea how to do that. I looked through the forums but I can't seem to find any solutions. Any suggestions would be helpful. B -- View this message in context:

Hi: In lattice, how does one handle separate graphical behavior for two different factors? In the xyplot below, the objective is to use the levels of one factor to distinguish corresponding shapes and colors, and the levels of the other factor to perform level-wise loess smooths. # Illustrative data: d <- data.frame(time = rep(1:8, each = 6), val = rnorm(48), gp1 =

Hi R, I want to do some rolling correlations. But before, I searched for "?rollingCorrelation" and tried the example in it. But I was not successful. What could be the problem? Here is the code I tried: > library(zoo) > library(PerformanceAnalytics) > rollingCorrelation(manager.ts@Data[,1],edhec.ts@Data,n=12) Error in inherits(object, "zoo") : object

Dear all, I have spent the last few days on a seemingly simple and previously documented rolling regression. I have a 60 year data set organized in a ts matrix. The matrix has 5 columns; cash_ret, epy1, ism1, spread1, unemp1 I have been able to come up with the following based on previous help threads. It seems to work fine. The trouble is I get regression coefficients but need the immediate

Hi everyone, I am trying to apply linear regression to adjacent columns in a matrix (i.e. col1~col2; col3~col4; etc.). The columns in my matrix come with identifiers at the top of each column, but when I try to use these identifiers to reference the columns in the regression function using rollapply(), the columns are not recognised and the regression breaks down. Is there a more robust way to

Hi, I would like suggestions as to how to perform rolling regressions with the window extended one period at a time. That is, an initial sample period is passed to estimation and that very sample is then extended one period at a time through the remaining sample. Is there a specific package? Thnks. P. -- View this message in context:

Hi All, I am using R 2.11.0 on a Ubuntu machine. I have a time series data set and want to run rolling regressions with it. Any suggestions would be useful. Here are the details: (1) I convert relevant variables into time series objects and compute first differences: vad <- ts(data$ALLGVA/data$GDPDEF, start=1948, frequency=1) emp <- ts(data$ALLEMP, start=1948, frequency=1) vad.dif1 <-

Could someone give me an idea on how to do rolling ranking, i.e. rank in the moving window of last 100 numbers in a long vector? I tried naive solution like roll.rank<-function(v, len){ r<-numeric(length(v)-len+1) for(i in len:length(v)) r[i-len+1]<-rank(v[(i-len+1):i])[len] r } However, it turns out pretty slow even on my rather able Linux box. For

I am looking for some help at removing low-frequency components from a signal, through Moving Average on a sliding window. I understand thiis is a smoothing procedure that I never done in my life before .. sigh. I searched R archives and found "rollmean", "MovingAverages {TTR}", "SymmetricMA". None of the above mantioned functions seems to accept the smoothing