similar to: Seasonal analysis

Hi Irucka, No problem. I guess this could be done using: Sample data: Dailydo<- structure(list(Date = structure(c(11231, 11232, 11233, 11234, 11235, 11236, 11207, 11208, 11209, 11179, 11180, 11181, 11151, 11152, 11123, 11093, 11064, 11065, 11035, 11036, 11008, 11009, 10979, 10980, 11347, 11348, 11380, 11381, 11410, 11406, 11438, 11439, 11470, 11502, 11528, 11560, 11561, 11593, 11594,

Hi, I have a new workstation and am in the process of moving all my work onto it. I have also installed R 2.10.0 on the new machine. I remember changing my R settings of my old machine (R 2.9.1) to connect to the internet, but I can't remember exactly how. I have tried Sys.getenv(), but it does not give me the proxy settings I would need to finish the setup of my new machine. Does anyone

Hi there, I have a piece of Matlab code I use to optimise a trding strategy. If there are any Matlab/R specialists out there, I would appreciate your help in doing the exact same optimisation in R. I suspect I would use nlm() in R but am not sure where to define my constraints. I have attached my Matlab code below for reference. Many thanks. Constraints function [c,ceq]=TriskellConstraints(X)

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hi R-help, I'd like to extract the seasonal component of a short timeseries, and was hoping to use stats::decompose. I don't want to decompose the 'trend' component so I thought I should call decompose(x,filter=0). I think I've either misunderstood the filter argument or come upon a bug/feature in decompose. # EXAMPLE

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

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

Thanks for everyone's help with zoo -- I think I've got my data set ready. (The data consists of surface weather temperatures, from 2002 to 2005, one observation per hour. Some values are missing... i.e. NA) I have three goals: GOAL #1:Get the data in proper time series form, preserving frequency information: > w4.ts <- as.ts( w3.zoo, frequency=(1/3600) ) I hope that 1/3600

Hello, does anyone know about: a) simulating seasonal ARIMA models? arima out of package ts can fit it, but it does not look like it can simulates data from seasonal models b) fitting and simulating fractional seasonal ARIMA models? Hints will be appreciated, Henning -- Henning Rust Potsdam Institute for Climate Impact Research Dept. Integrated Systems Analysis Tel.: #49/331/288-2596

Hi all, I was wondering how to construct a seasonal differenced time series variable. I used the following code to construct a 12 span seasonal difference seasonal<-diff(V2, lag=12, differences=1) is this correct? thank you in advance joe [[alternative HTML version deleted]]

Hello All, I trying to find some way to fill in missing values in a seasonal time series. All the function that I find until now, don't have any reference to seasonal data and the output is very different of what I looking for. I also searched the forum but this problem don't have many information or people asking. Could someone indicate some links or packages related to this question?

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

Hi, I have a set of measurements that are made on a daily basis over many years. I would like to produce a *non-parametric* smooth of these data to estimate the seasonal cycle - to achieve this, I have been using the cyclic cubic splines from the mgcv package. This works superbly in most situations, but not all. The problem is that for various practical reasons the data is not available all year

Hello everybody, I need to calculate seasonal means with temperature data for my work. I have 70 files coming from weather stations, which looks like this for example: startdate <- as.POSIXct("01/01/2006", format = "%d/%m/%Y") enddate <- as.POSIXct("05/01/2006", format = "%d/%m/%Y") date <- seq(from = startdate, to = enddate, by =

Hi list, I would like to estimate and forecast the seasonal component of a series. My model which uses daily data would be something y t = alpha + beta x SeasComp t + gamma x OtherRegressors t. One approach to this would be use quarterly dummies, another to use a sine function. The first would cause a step change when we move from a season to another; the latter impose too much regularity in

Hi all, I just couldn't find a R function which can fit multiple seasonal patters... i.e. in the following code: *arima(x = data, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), ... *** * there can be only one "period", am I right? What if the data seem to have three different seasonality cycles, 5, 12, 21? Thanks a lot! * [[alternative HTML version

Hi there, Using a quantmod function, I calculate the daily change between two points in a time series. However, I don't think I am using the data correctly. Code: getSymbols("^GSPC", src="yahoo") CloseData <- Cl(GSPC) Delta <- diff(CloseData, lag=1) for (i in 3:length(Delta)) { if (Delta[i]>Delta[i-1]) sum <- sum + Delta } I can't seem to use the Delta

Hello, I am new to usin R - which is a great tool - and would like to know if R has a seasonal adjustment program for time series and/if it incorporates the Census Bureau's ARIMA x-12 seasonal adjustment program in any way? Thanks so much! Tony [[alternative HTML version deleted]]

Are any seasonal adjustment programs, like Tramo/Seats, Census X12 ARIMA or Berliner Verfahren implemented in R? I am doing a simulation study and I don't know how to adjust the series in R. The possibility to access external the exe.files of the seasonal adjustment programs seems to be quite difficult. Can anyone help me? Thanks, Ingo