similar to: stl outlier help request

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

G'day, I am looking at monthly reports, and have three series of monthly data from 2007 to 2009. I would like to show the season decomposition of these two series side by side on the one device, however using plot doesn't seem to respect any use of layout(matrix(1:3, ncol=3)) or par(mfcol=c(1,3)). I'm guessing that this means that the plot(stl) perhaps uses them, but I can't find

Hi, I'm astudent in hydrobiology and I actually work on river's discharge and try to extract from my data the seasonal and trend components. I use STL function but I have several problems in understanding what this function have done. I'd like to know what means the IQR results which gave me some % about the seasonal component, trend component and the remainder component.

I having issues with interpreting the results of STL decomposition. The following is the data used as well as the decompsed seasonality, trend and the remainder components. It is a weekly data. The original data doesn't appear to be seasonal. But there seems to be a periodic peak in the seasonal component. Can some one please let me know how to interpret the seasonality plot in this??? Also,

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 there, My name is Renan X. Cortes, student of Statistics, from south of Brazil, and I'd like to ask you a few questions about decomposition of time series. In R, when I fit the decomposition using the "stl" function, an object is returned when ask the summary of the fit, called STL.seasonal (%), STL.trend (%) and STL.remainder (%). Once the decomposition is additive,

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

Hi all, I would like to adjust the monthplot() of an stl() so that for a time-series with freq=12 (months): a) the curve on the panel for the k-th month graphs the seasonal values minus their monthly mean values b) add to the fig. the values of the k-th month of the seasonal + remainder, also minus their monthly mean values which corresponds to the 'seasonal diagnostic plot as described by

Greetings: I'm using the time series decomposition routine "stl" from the package "stats". But how do I get the results into a vector to work with them? example: data(AirPassengers) m<-stl(AirPassengers,"per") print(m) This lists the output but can't figure out how to extract the individual series like seasonal, trend, irregular. Thanks, Bob

I need to investigate how rollmean and the trend returned from stl differ. I am trying to find out exactly what the trend part of stl is (I have just started coding in R and do not know fortran). I need to extract this because it will be used in further calculations, and it needs to be verified to make sure that I am using the right process. I would like to use this to remove the seasonal

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

Hi all, I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First,

Hi All, How do I can create a new vector, i.e. 'seasonal' or 'trend' from the resultant seasonal or trend component of the Time.Series object produced by STL, and how I could superpose in the same graphic, i.e. original data and trend or seasonality? Thanks in advance! Antonio Antonio Rodr?guez Verdugo CICEM Agua del Pino Huelva Oceanography and Coastal Resources, PhD Program,

Hello, anyone got an idea on how to use stl() so that the remainder eventually becomes white noise? i used stl repeatedly but there is autocorrelation in the remainder that i can't get rid of. os: linux suse9.3 ------------------------------------------------ Sebastian Leuzinger Institute of Botany, University of Basel Sch??nbeinstr. 6 CH-4056 Basel ph 0041 (0) 61 2673511 fax 0041 (0)

I am having trouble plotting outliers on time series. Give then following code: ############################################################ # find STL Outliers by weight and append sh2, use Robust # this should allow the initial outliers to be filtered # this section may be commented out. ############################################################ tsSourceDiag <-

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

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!

Hi all, I'm working on decomposition and comparison of several time series. I'm interested in extracting the trend components for each time series using the stl function and overlaying them on one another. I'm not sure how to plot the trend function alone and to do the overlay using some kind of loop. If anyone has any insight that would be great! thanks, Katie -- View this

Dear list, R-core, The documentation of stats::stl explicitly refers to the paper by Cleveland[1] to explain the parameters. However, the description is confusing, with two descriptions seeming to refer to the same parameter in the paper. s.window: [...] the loess window for seasonal extraction, which should be odd and at least 7, according to Cleveland et al --> The phrase 'odd and at