similar to: Understanding TS objects

Displaying 20 results from an estimated 20000 matches similar to: "Understanding TS objects"

2010 Oct 12
1
Help with STL function to decompose
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
2003 Jul 30
2
STL- TimeSeries Decomposition
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!
2006 Jul 13
1
ts and stl functions - still a problem
Hi I am still having problems with using the stl function, when I read the csv file into R into a file called tkr and use dim(tkr) the result is 132 1 which is fine. When coerce it into a trime series using ts either: tstkr <- ts(t(tkr), deltat=1/12) or tstkr <- ts(c(tkr), deltat=1/12) and use the stl function I get the following error: Error in
2011 Dec 14
1
series temporales. índices de variación estacional
Hola. Estoy empezando a ver temas de series temporales y me gustaría saber como obtener con R los IVES (índices de variación estacionales). Por el momento estoy viendo cosas muy simples, como descomponer una serie en tendencia y componente estacional, usando la función decompose y también la función stl. Por ejemplo # generamos 48 datos de una normal por ejemplo x <- rnorm(48) # creamos el
2012 Aug 09
1
POSIXct to ts
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,
2010 Feb 07
2
predicting with stl() decomposition
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 <-
2006 Jul 12
1
ts and stl functions
Hi, I have imported a csv file into R which contains one column (the rate er 100,000 population of a disease, by month over 11 years) I coerced into a time series using the following function, tstkr<-ts(tkr,deltat=1/12) This seems to work fine, and when I check for the class of the object using class(tstkr) I get "ts" as the response. When I try to use the stl function in
2006 Oct 12
3
ts vs zoo
> Hello, > > I have lots of data in zoo format and would like to do some time > series analysis. (using library(zoo), library(ts) ) > > My data is usually from one year, and I try for example stl() to find > some seasonalities or trends. > > I have now accepted, that I might have to convert my series into ts() > but still I am not able to execute the comand since
2008 Sep 02
2
More help with stl?
I don't understand the output of stl. As a simple example: y <- numeric(1:365) y[250] = 1 stl <- stl(ts(y, frequency=7), s.window="periodic") This returns without error but the results are puzzling to me. If you plot the results it is probably easiest to visualize what I mean. plot(stl) This shows the original data (a single spike at 250). A trend (which also shows a bump
2011 Mar 04
3
Análisis de una serie temporal diaria
Hola a todos Estoy tratando de analizaruna serie temporal con datos diarios de temperaturas (40 años). He creado un objeto zoo (con ayuda de la lista, gracias) sobre el que encuentro la regresión lineal. He probado también a crear un objeto ts a partir del zoo. El problema que encuentro es que nose puede aplicar la función stl para hallar la componente estacional y la tendencia. Rdice que la
2001 May 16
1
stl in library(ts)
I am running R 1.2.2 under Linux. When using the function stl in the ts library, how can I save the seasonal component? What I would like was something like: library(ts) data(nottem) data.stl <- stl(nottem, "per") x <- data.stl$sea This what I get: > x NULL I would, however, like to store in x the seasonal component. Thanks in advance. Francisco. -- Francisco
2011 Dec 01
3
Assign name to object for each iteration in a loop.
Hi R-users, I'm trying to produce decompositions of a multiple time-series, grouped by a factor (called "area"). I'm modifying the code in the STLperArea function of package ndvits, as this function only plots produces stl plots, it does not return the underlying data. I want to extract the trend component of each decomposition ("x$time.series[,trend]), assign a name
2009 Nov 27
1
Time Series Data
Hi All, I'm trying to analyze some time series data and I have run into difficulty. I have decadal sun spot data and I want to separate the very regular periodic function from the trend and noise. I looked into using stl(), but the frequency of the time series data must be greater than 1 for stl(). My data covers a 1000 year interval from 9095 BP to 8095BP and the frequency is, therefore, 0.1
2003 Oct 22
1
Help with STL function in R compared to S-Plus
I am trying to understand the nuances of STL (seasonal trend decomposition with loess) based on William Cleveland's (and others?) original development. I do not understand the specification or use of "frequency components" or equivalent "low-pass filter" components in the stl() function. I have run the stl() function on a standard example data (co2) in both S-Plus and
2017 Nov 08
2
Help Converting Calendars
R-Help Trying to convert a Gregorian calendar dataset to a Persian calendar dataset. But I end up with a list and not sure what to do. For example ... dates <- c("2017-10-1","2017-10-2","2017-10-3") myData <- data.frame(dates) myData$dates <- as.Date(myData$dates, format = "%Y-%m-%d") > myData dates 1 2017-10-01 2 2017-10-02 3
2011 Aug 26
1
Time Series data with data every half hour
I am working with data from the USGS with data every 30 minutes from 4/27/2011 to 8/25/2011. I am having trouble with setting the frequency. My R script is below: > shavers=read.csv("shavers.csv") > names(shavers) [1] "agency_cd" "site_no" "datetime" "tz_cd" "Temp" [6] "X04_00010_cd"
2018 Apr 12
3
Bivariate Normal Distribution Plots
R-Help I am attempting to create a series of bivariate normal distributions. So using the mvtnorm library I have created the following code ... # Standard deviations and correlation sig_x <- 1 sig_y <- 1 rho_xy <- 0.0 # Covariance between X and Y sig_xy <- rho_xy * sig_x *sig_y # Covariance matrix Sigma_xy <- matrix(c(sig_x ^ 2, sig_xy, sig_xy, sig_y ^ 2), nrow = 2, ncol = 2)
2004 Jul 21
2
Testing autocorrelation & heteroskedasticity of residuals in ts
Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it
2001 Dec 23
1
Time Series Data
Happy Holidays All, I'm trying to use stl() to extract a seasonal component from a time series. I believe the root cause of my problem lies in how I imported the data into R. In Excel (csv), the data look like: Jan Feb Mar .... 1949 12.2 9.0 7.9 .... 1950 17.2 16.9 9.9 .... 1951 8.2 7.0 7.7 .... I import to R using:
2017 Nov 08
0
Help Converting Calendars
How about > p_dates <- paste0(p.dates[[3]], "-", p.dates[[2]], "-", p.dates[[1]]) > myData$p_dates <- p_dates > print(myData, right=FALSE) dates p_dates 1 2017-10-01 1396-7-9 2 2017-10-02 1396-7-10 3 2017-10-03 1396-7-11 > str(myData) 'data.frame': 3 obs. of 2 variables: $ dates : Date, format: "2017-10-01"