similar to: adjusting monthplot() towards a seasonal diagnostic plot for stl()

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, I try to figure out what the stl-function exactly do. I was reading the paper by Cleveland et al. (1990) and tested some features of stl (the ability to decompose time series with missing values and the robustness feature). I tried the following: > data(co2) > co2.na <- co2 > is.na(co2.na[c(50, 100)]) <- TRUE > plot(stl(co2.na, s.window = 12, na.action = na.exclude)) With

The monthplot function in S is useful for plotting seasonal data. It's not in base R or the ts package; is it in one of the contributed packages? (I've written a quick version for myself; if there isn't one generally available already, I'll make it public somehow.) Duncan Murdoch -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list

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,

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

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 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

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 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 help, I'm working with regular 8-daily time-series from 2000 up till now and would like to be able to compare years with each other. E.g. by creating a monthplot via the result of the stl() method it looks ok> but I was wondering whether there exist other methods to plot the different years as lines on top of each other such that years can be compared with each other (temporal

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,

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 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

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, How can the label of the x-axes in the plot() of a stl.object be adapted? e.g., When plotting: plot(stl(nottem, "per")) In the labels of the x-axes is “time”. How can this be changed to e.g., “Time (dekade) “? It does not work with xlab or others anymore… Thanks, Jan _______________________________________________________________________ Ir. Jan Verbesselt Research

Hi R-Helpers, I'm dealing with the STL procedure and trying to apply the tsdiag and StructTS onto the ts object to analyse the different parameters which need to be set. How can I use the tsStructure & tsdiag to create a seasonal, trend and cycle subseries plot so that I can select & analyse the correct np,ns, nt and nl? The problem is that too much signal goes into the seasonal

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 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,

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