similar to: irregular time series prediction

There has been a few questions on the subject lately. Is there any book on the subject, if possible with a computer processing flavor, that you would highly recommend? Many thanks in advance, -- Jean-Luc

Hello, Is there any software project that uses R to do log file analisys? thanks gabi

Working through the R archives and webspace, I've mostly proved to myself that I don't know enough about what statisticians call "Curve Fitting" to even begin translating the basics. I'm a sysadmin, and have collected a variety of measurements of my systems, and I can draw pretty pictures in R showing what has happened. People are happy, customers feel empowered. Whee!

Dear R users, I have a quick question regarding creating an index for a zoo object. I am using R 2.12 on winxp. I have read through the R archives searching for information on dates and time series analysis and nothing seems to cover my question. I am extracting data from a time series of remote sensing data. The frequency of the data is 16 days which means I cant use ts (ts likes months single

Hello, I have a series of intraday (high-frequency) price data in the form of POSIX timestamp followed by the value. I sucesfuly loaded that into "its" package object. I would like to create from it a regularly spaced time series of prices (for example 1min, 5min, etc apart) so i could calcualte returns. There is an interpolation function locf() that for timestamp with value NA uses last

Hi, I realized that when I have irregular series to feed into lineChart, the interval of each point in the chart does not seem to take care of irregular time interval I specified in my input xts time series. But rather, lineChart seems to take each point as equal spaced time series. For example, I have the following code: library(quantmod) options(digits.sec=3) t0 <-

I am new to R and am trying to solve the following problem: I have a data file containing tick-by-tick, millisecond level prices for some stocks. I have another file or two containing orders and trades, again, with millisecond time-stamps. Both of these files are irregularly spaced and the time stamps are in an iso format (<date> <time>.<millisecond>) I would like to create a

Howdy! I am attempting to convert a date frame with irregular dates into a regular time series, aggregated by date. i have tried using both the 'its' and 'zoo' packages. I have something like times<-c("2003-03-05", "2003-03-05", "2003-05-05" ,"2003-04-07" ,"2003-03-05") aarf<-data.frame(times) aarf$x<-runif(5) what i

Greetings -- I've got some sensor data of the form t1_1, t1_2 t2_1, t2_2 ... tN_1,tN_2 -- time intervals measuring starts and stops of sensor activity. I'd like to see whether there's any regularity in it. Seems natural to consider these data timeseries -- except most of the timeseries packages and models assume regular ones, with a fixed frequency. I wonder what's a

Hi R Users! I would like to create an high frequency series but I am experiencing some difficulties. My series should start at 09.30 a.m. each day and end at 16.00 for, let's say, 2 years. I don't care on how many observations are for each day. It's ok also one observation each minute. In this case, I would have 390 observations each day. I have tried the following: start <-

Background: OS: Linux Mandrake 9.1 release: R 1.9.0 editor: Xemacs 21.4 frontend: ESS 5.1.23 --------------------------------- Colleagues I have two time series (upwelling index and water temperature) of evenly spaced, daily data over 18 months, but the upwelling index series has a gap of about 2 months right in the middle of it. I want to do the acf, pacf, ccf, and a cross-spectral analysis

I built some reasonably successful tools in a graphical database for reconstructing the developmental turning points for feedback loop driven natural processes. I'm trying to move it to R and am having difficulty with the very basics, i.e. a) defining time series using time as a natural number rather than a place in a table. I was also hoping b) someone might have built some of the

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print, plot, extraction, append, and related functionality are available. Feedback and suggestions are welcome. Giles Heywood

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print, plot, extraction, append, and related functionality are available. Feedback and suggestions are welcome. Giles Heywood

Hello, I'd like to make a z(x,y) plot for irregularly spaced x,y. What are routines are available in R for this purpose? Thanks, Mark

Dear R users I am currently investigating time series analysis using an irregular time series. Our study is looking at vegetation change in areas of alien vegetation growth after clearing events. The irregular time series is sourced from Landsat ETM+ data, over a six year period I have 38 scenes. For certain periods I have monthly data while for others, images are up to three months apart. So far

hi guys: sorry, one more. I have irregularly spaced time series, often with big gaps. a good analogy is: tsvec <- ts(c(1,NA,2,NA,1, NA, 2),freq=12, start=c(1965,12)) Unfortunately, plot( tsvec ) does not plot the data. is it possible to convince plot to just ignore the NA items (either with points or lines) help appreciated. /iaw

A new version of tseries (0.9-10) has been uploaded to CRAN. The new version contains the class "irts" for irregularly spaced time series. Irregular time series are basically time series where each observation (uni- or multivariate) has a time-stamp represented by an object of class "POSIXct". It provides some basic functionality such as reading and writing irregular time

A new version of tseries (0.9-10) has been uploaded to CRAN. The new version contains the class "irts" for irregularly spaced time series. Irregular time series are basically time series where each observation (uni- or multivariate) has a time-stamp represented by an object of class "POSIXct". It provides some basic functionality such as reading and writing irregular time

Dear R users, I have two irregular time series say x and y. Each series is supposed to cover 20 years. The data looks, for instance: x<-c(200,178, 330, 127, 420) ## only 5 observations out of the expected 20 annual values y<-c(0.35,-0.18,-0.54,0.78,1.7,-1.1,0.2,1.9,0.49)### only 9 observations of the expected 20 I need to intepolate each of the series into equally spaced 20 points. Is