similar to: aggregating values at discreet irregular time intervals into hourly values

I make quite a lot of use of irregular time-series, and had already spent a bit of time writing an 'its' class when the 'irts' class was released via the package 'tseries'. I have experimented with the 'irts' class, and have some practical issues with its use. In some applications of irregular time-series (in my case these are financial and econometric) there are

I'm having some problems with aggregate.ts, e.g. R> x <- ts(1:20) R> frequency(x) [1] 1 R> aggregate(x, nfreq=1/3) Error in aggregate.ts(x, nfreq = 1/3) : cannot change frequency from 1 to 0.333333333333333 In fact aggregate.ts only accepts a new frequency that is a negative power of two in this example. The problem with the current test for compatible frequencies if

Hi R-list, I'm working with irregular time series (time series of climate data, daily data 365/6 days a year) and would like to create regular time series from them ( irts e.g. Rain <- irts(as.POSIXct(Climate[,1]),Climate[,5]) to ts e.g. test <- ts(x, start=c(1997,1), frequency=365) ) such that I can find where the gaps (lacking temperature data, ...) are and try

Dear R-helpers, I'm trying to associate linear coefficients (intercept and slope) to tens of thousands of observations based on a table with benchmark values. #####Example - Value table and their corresponding coefficients (intercept and slope) coef = data.frame(cbind(st=c(1:5),b = runif(5,0.3,5),a = seq(0.5,5,1)))print(coef) #Example of observations to be computedobs = runif(20,1,5)print(obs)

Hi, I have a ts object with a frequency of 4, i.e., quarterly data, and I would like to calculate the mean for each quarter. So for example: > ts.data=ts(1:20,start=c(1984,2),frequency=4) > ts.data Qtr1 Qtr2 Qtr3 Qtr4 1984 1 2 3 1985 4 5 6 7 1986 8 9 10 11 1987 12 13 14 15 1988 16 17 18 19 1989 20 If I do this manually, the mean

R: Is there an easy way to get the acf and pacf for an irregular times series? That is, the acf and pacf with lag lengths that are in units of time, not observation number. Thanks, Jason Higbee Research Associate Federal Reserve Bank of St. Louis The views expressed in this email are the author's and not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve

Hi, I have a couple of aggregation operations that I don't know how to accomplish. Let me give an example. I have the following irregular time series time x 10:00:00.021 20 10:00:00.224 20 10:00:01.002 19 10:00:02:948 20 1) For each entry time, I'd like to get sum of x for the next 2 seconds

I'm probably apporaching this all wrong to start but.... Suppose I have a monthly time series and I want to compute the mean of months 6,7, and 8. I want to plot the original time series and the seasonal time series, one above the other. When I do that as below the time series don't line up for reasons that are obvious. How can I change the base of the seasonal time series so I can make

Hi All, I have a data set of daily measurements of river flow. I would like to create a "ts" object from this data. Here's a sample data set: date <- as.Date(c(1:300), format="%Y") year=as.numeric(format(date, format = "%Y")) month=as.numeric(format(date, format = "%m")) julianday=as.numeric(format(date, format = "%j"))

A new version of tseries (0.9-10) has been uploaded to CRAN. The new version contains the class "irts" for irregularly spaced time series. Irregular time series are basically time series where each observation (uni- or multivariate) has a time-stamp represented by an object of class "POSIXct". It provides some basic functionality such as reading and writing irregular time

A new version of tseries (0.9-10) has been uploaded to CRAN. The new version contains the class "irts" for irregularly spaced time series. Irregular time series are basically time series where each observation (uni- or multivariate) has a time-stamp represented by an object of class "POSIXct". It provides some basic functionality such as reading and writing irregular time

I have some daily data and would like to apply some of the time series functions to it. I have read various notes in the help archive on this, the latest I found suggested that I need to use the irts class (Irregularly spaced time series) for daily data since a year does not divide into an integer number of days. I see why I would have to do that if I have gaps (e.g. only data on weekdays) but

Hi, Is it possible to create a ts() object, whose data is daily based BUT measured only on working days? In other words, suppose I have a data set with 255 observations, measured from 29 June 1959 to 30 June 1960. How would I create such a data? I tried something like: ts(c(...), start(1959, 180)) but I'm not sure what to use for frequency. In other words I don't know how to

Hi all, I've got some problems with irts objects, one of which could be a bug: 1) Read a table with several columns from Postgres and the first column is Timestamp with timezone (this is OK). An extract is: raincida$ts: [2039] "25/03/2000 22:00:00 UTC" "25/03/2000 23:00:00 UTC" [2041] "26/03/2000 00:00:00 UTC" "26/03/2000 01:00:00 UTC" [2043]

Dear R users, I'm trying to use the "strucchange" package to determine structural breaks in an ARFIMA model. Unfortunately I'm not so familiar with this topic (and worse, I'm a beginner in R), so I don't know exactly how to specify my model so that the "Fstats","sctest" and "breakpoint" functions to recognize it and to calculate the

I know this: > library(date) > x="1979-04-04" > try=as.date(x, "ymd") > print(try) [1] 4Apr79 and that `x' here has to be a string, e.g.: > x=1979-04-04 > print(x) [1] 1971 I'm stuck in reading from a file. I say: > A <- read.table(file="try") > print(A) V1 V2 1 1979-04-04

I thought r-help let you attach asci files but I don't think it does now so below is a sample of my data set. Thanks again. 09:40:08.5238,67.00,33 09:40:09.1968,67.00,2 09:40:09.7945,67.00,2 09:40:09.7975,67.00,2 09:40:09.8318,66.99,-3 09:40:17.6335,66.95,3 09:41:09.3393,66.95,6 09:41:11.1482,66.95,-1 09:42:07.4552,66.90,-5 09:42:12.5823,66.85,-5 09:42:14.4329,66.80,-2

> S-Plus has the function AggregateSeries() whose name is self > explanatory. For instance one can derive monthly series from daily > ones by specifying end-of-period, averages, sums, etc. I looked for > a similar function in the packages "its" and "tseries", but found > nothing. I also help.searched() for aggregate to no avail. Would > anybody be so kind

aggregate.ts does not behave in the same way as the equivalent method aggregate.rts in S-PLUS. In particular it - changes the start of the time series - tends to have a length which is 1 shorter For example: R> x <- ts(1:10) R> aggregate(x, nfreq=0.5, FUN=min) Time Series: Start = 2 End = 8 Frequency = 0.5 [1] 2 4 6 8 S> x <- rts(1:10) S> aggregate(x, nf=0.5, fun = min) [1]

Having to collect hourly electricity loads and quarter-of-an-hour electricity production data for some years I think that the tidiest way of doing it is to resort to ts but I don't know how to define such a frequency starting from a set date. Leafing through r-help mail archives I've found this *ALMOST* satisfactory message: ==========================================================