similar to: ts; decompose; plot and title

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 <-

InDATA <-read.table("C:/Data/May 2004/season.txt",header=T) X <- decompose(InDATA) print(X) Period Connections Q1 67519 Q2 69713 Q3 68920 Q4 69452 Q1 70015 Q2 59273 Q3 57063 Q4 65596 Q1 73527 Q2 58586 Q3 69522 Q4 60091 Q1 51686 Q2 63490 Q3 55702 Q4 53200 Q1 51033 Q2 48175 Q3 52709 Q4 50106 Q1 50855 Q2 43466 Q3 48190 Q4 41702 Q1 48747 Q2 51441 Q3 42537

Hi, I have a dataframe (try.1) with  date/time and temperature columns, and the date/time is in POSIXct fomat. Sample included below. I would like to to try decompose () or stl() to look at the trends and seasonality in my data, eventually so that  I can  look at autocorrelation.  The series is 3 years of water temperature with clearly visible seasonal periods. Right now, if I try decompose,

Hi All, I am new to this forum. I have just started learning R. When i use plot(decompose(x)), i am getting the title " Additive time series decomposition". How to make this title off and change to some other title. Any help regarding this is highly appreciated. With sincerer regards, Upananda -- View this message in context:

The decompose() function truncates the seasonal component unnecessarily. I've modified the function to fix this problem, and also added the original data to the object returned (to enable better plotting). I've also modified the plot.decomposed.ts() function so that it plots the original data in the top panel rather than the reconstructed data. The difference between the two is that the

Dear R People: In the function "decompose", the object has the class of "decomposed.ts". (from package stats) I would like to see the class definition and the method for the plotting. However, when I use isClass("decomposed.ts") I get "FALSE". When I check getMethods("plot") there is no method for plot on decomposed.ts Any suggestions,

Hi, I asked this question before, which was hidden in a bunch of questions. I repharse it here and hope I can get some help this time: I have 2 contingency tables which have the same group variable Y. I want to compare the strength of association between X1/Y and X2/Y. I am not sure if comparing p-values IS the way even though the probability of seeing such "weird" observation under H0

Hello, I'd like to know how R does calculate each component in the decompose() function? More precisely, how is calculated the final trend component in this function? Thanks for your answer Myriam -- View this message in context: http://www.nabble.com/Decompose-function-%3A-calculation-of-each-component-tp24362207p24362207.html Sent from the R help mailing list archive at Nabble.com.

I just got this error: > library(igraph) > comp <- decompose.graph(gr) Error: protect(): protection stack overflow Error: protect(): protection stack overflow > what can I do? the digraph is, indeed, large (300,000 vertexes), but there are very many very small components (which I would rather not discard). PS. the doc for decompose.graph does not say which mode is the default. --

i am new to time series,whatever i know up till now,from that i have uploaded time series file & what to build arma model,but for that i want p & q values(orders) tell me how to calculate best p & q values to find best AIC values for model i am doing but giving error >bhavar<-read.table(file.choose()) #taking time series file > decompose(bhavar$V1) Error in

Greetings, For the following quarterly data I did a classical decomposition by hand in a spreadsheet and got reasonably similar results using Minitab 15. x 1 36 2 44 3 45 4 106 5 38 6 46 7 47 8 112 9 42 10 49 11 48 12 118 13 42 14 50 15

Dear R Users, I am doing stats::decompose() on 4 different time series. When I issue csdA <- decompose(tsA) plot(csdA) I get a summary plot for observed, trend, seasonal and random components of decomposed time series tsA. As I understand it, the object returned by decompose() has it's own plot method where mfrow(4,1) etc. is defined. Now suppose I wanted to wrap those mfrow(4,1) into

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need

Is there a simple way to decompose the upper triangle of a correlation matrix to a linear list; For example: X Y Z X 1 2 3 Y 2 1 4 Z 3 4 1 so you get a list like: xy 2 XZ 3 YZ 4 I suspect you can do it with a matrix transformation, but that beyond me at present. Many thanks Mark _________________________ Department of Molecular and Human Genetics, Baylor College of Medicine,

Dear R Helpers, Currently I'm working with the ts package of R and created a TimeSerie from pixels extracted from satellite imagery(S10 NDVI data, 10 daily composites). I'm trying to decompose this signal in different signals (seasonal and trend). When testing out the STL method is says => Only univariate timeseries are allowed, but the current Timeserie I'm using is univariate!

Dear R users, I have a very simple problem but I can't find the function in R to deal with it. I need to split (or decompose) one line into many lines using one field as a reference. I have a table with the following format: A B Frequency 23 3 2 24 2 5 25 1 3 And need to split each line into several lines according to the frequency to achieve something like this: A B

i have data in one file below like & (i have such type of file =200,each file have below type of data) >t -0.15264004 0.056076439 -0.07276116 -0.00917326 -0.02069089 -0.00416232 -0.07225855 -0.02654577 -0.06131410 -0.09380202 0.057414014 -0.05239976 0.014397612 0.016145161 -0.00670587 0.018696335 0.036943654 -0.02450233 0.031161705 0.006513503 -0.02892329 -0.00831519 -0.00877744

Hi everyone. I'm having some troubles with STL function to decompose some data. My issue is that I have monthly data from September 2005 up to August 2010 i. e. 60 observations. I define it in the following way: *u<-read.csv("C:/CELEBREX.csv",header = TRUE) u.ts<-ts(u, start=c(2005,9), frequency=12) * The issue is that when I try to use stl(u.ts, 'per') Then the

Dear all, My data is by minutes and I can see it has seasonal trend by daily and weekly. How do I decompose the minute data into daily and weekly some data: > dput(tail(dt_train,100))structure(c(11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 10L,

every time i try to run HoltWinters i get this error message: > HoltWinters(z, seasonal="additive") Error in decompose(ts(x[1:wind], start = start(x), frequency = f), seasonal) : time series has no or less than 3 periods what's going on? somebody please help me. -- View this message in context: