similar to: multiple seasonal time series models

Hi there, I am a graduate student using "R" for time series modeling. I have a weeks data with 96 data per day. I am trying to use a seasonal model with period of 96 (the size of the total data is 480) to fit the data after deriving the order information from ACF and PACF plots. But, I am getting the following error message "Error in optim(init[mask] ...) : non-finite

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, The purpose of this e-mail is to ask about R time series procedures - as a biologist with only basic time series knowledge and about a year's experience in R. I have been using ARIMAX models with seasonal components on seasonal data. However I am now moving on to annual data (with only 34 time points) and understand that ARIMA is not suitable for these shorter time periods - does

Hi there, I am using the "ts" class of the "R" package. I have a data file containing a time series of 1152 entries. It is actually 4 days data cascaded together where the time interval of data collected is 5 minutes. Thus, I have 288 values per day. When I am trying to fit this data using a period of 288 with a seasonal model, using the arima(...) function, I am

i am new to time series,whatever i know up till now,from that i have uploaded time series file & what to build arma model,but for that i want p & q values(orders) tell me how to calculate best p & q values to find best AIC values for model i am doing but giving error >bhavar<-read.table(file.choose()) #taking time series file > decompose(bhavar$V1) Error in

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?

I have a seasonal time series. I want to calculate the annual mean value of the time series at its peak (say the mean of the three values before the peak, the peak, and the three values after the peak). The peak of the time series might change cycle slightly from year to year. # E.g., nPts <- 254 foo <- sin((2 * pi * 1/24) * 1:nPts) foo <- foo + rnorm(nPts, 0, 0.05) bar <- ts(foo,

Dear R list, I have a sequence of weekly observations of number of adults and larvae in various size classes from a butterfly population living in a subtropical area with pronounced wet and dry seasons. Wet and dry seasons are each defined 26 weeks long with fixed start and end dates. The data span 103 weeks (two seasons each of wet and dry) with some missing weeks. What I would like to do is

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

hello, I am using arima to evaluate a time series regression model. I am using categorical variables such as day of week(Su-Sa), month(Jan-Dec), and holiday status (1/0) as my independent variables. There is evidence of multiplicative interaction between holiday status and weekday and I would like to add an interaction term to my arima model, but I am not sure about best way to go about doing it.

Hi R masters, In my work I analyse a time serie of number of birth in State of Rio de Janeiro. After study de ACF e p ACF I conclude the model is: Non seasonal ar(1) In 7 days lag 7 days seasonal ma(1) In 364 days lag 364 days seasonal ma(1) If the time serie was Non seasonal ar(1) with one seasonal ma(1) is simple using command arima for fit a time serie, but I don't know HOW TO fit a

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

Dear R-help, How could a cross-correlation plot be optimized such that the relationship between seasonal time-series can be studied? We are working with strong seasonal time-series and derived a cross-correlation plot to study the relationship between time-series. The seasonal variation however strongly influences the cross-correlation plot and the plot seems to be ?rather? symmetrical (max

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 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 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,

I am fittling a spline to a variable in a regression model, I am then using the predict.glm funtion to make some predictions. When I use bs to fit the spline I don't have any problems using the predict.glm function however when I use ns I get the following error: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for

All, I am trying to include a day of week variable (1-7) in in a regression model. I would like to have the day of week treated as a categorical variable rather than a number the code looks like lm( dep ~ WKDY) I know this is a basic question, but help would be appreciated thanks spencer [[alternative HTML version deleted]]

Hi friends, If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at : CRAN: https://cran.r-project.org/web/packages/PSF/index.html GitHub: https://github.com/neerajdhanraj/PSF How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm and

Hi friends, If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at : CRAN: https://cran.r-project.org/web/packages/PSF/index.html GitHub: https://github.com/neerajdhanraj/PSF How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm and