similar to: Best forecasting methods with Time Series ?

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

I have been trying to import a csv file to r. but I get the same message everytime. the message is Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file 'Users:/sezginozcan/Downloads/beer.data.csv': No such file or directory I use mac. I tried this command also

Hi List, I am running R 2.8.0 on a Windows XP machine, running ggplot2 version 0.8.1 I want to label the bars in a faceted grid barplot. Reproducible R code is given below: #### reproducible facet barplot ##### library(ggplot2) # Dataset from which to create the barplot ml <- rep(1:10,2) vals <- rnorm(20,mean = 10, sd=1) type <- c(rep("MAPE",10),rep("AIC",10))

I would like the text plotted with 'mtext' to be alighned like it is for printing on the console. Here is what I have: > print(emt) ME RMSE MAE MPE MAPE MASE original -1.034568e+07 1.097695e+08 2.433160e+07 -31.30554 37.47713 1.5100050 xreg 1.561235e+01 2.008599e+03 9.089473e+02 267.05490 280.66734

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hi again With arima0 the problem was solved but what are the diferences between arima and arima0? I have another question. I fit the model to the data and I make some predictions. But I also want to calculate MAPE based in the last 3 observations available. Is it possible? Can I obtain the fitted values from the model? thanks~ luis -- SAPO ADSL.PT, apanhe j? o comboio da Banda Larga. Kit

Hey, I have a data frame x which consists of say 10 vectors. I essentially want to find out the best fit exponential smoothing for each of the vectors. The problem while I'm getting results when i say > lapply(x,ets) I am getting an error when I say >> myprint function(x) { for(i in 1:length(x)) { ets(x[i],model="AZZ",opt.crit=c("amse")) } } The error message is

Hi, I would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model =

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >

Hi, i have a weekly data for servers for 62 weeks. want to predict the cpu% for next 5 weeks.I am trying to forecast for many servers at once but with the code i am getting only one week of future forecast for all the servers. Also the week date for the predicted week is showing as the last week of the original data . Need help in two things How can i change the date for the predicted week, and

Hi I am trying to forecast for multiple server for CPU Utilization , But currently i am getting it for for one week , when ever i am trying to get more than one week it gives me error. Secondly in the one week forecast values i am getting the weekend date as the last week end in the original data set , where as it should be one week after that . Below is my code Let me know if anything else is

Rob J Hyndman gives great explanation here (https://robjhyndman.com/hyndsight/estimation/) for reasons why results from R's arima may differ from other softwares. @iacobus, to cite one, 'Major discrepancies between R and Stata for ARIMA' (https://stackoverflow.com/questions/22443395/major-discrepancies-between-r-and-stata-for-arima), assign the, sometimes, big diferences from R

hi all i want to provide audio recording facility with my Ror application , which can be stored in DB at server , for later use can anybody tell, how to implement this with RoR, as i dont hav much idea abt that thanx --------------------------------- Yahoo! Mail Bring photos to life! New PhotoMail makes sharing a breeze. -------------- next part -------------- An HTML attachment

Dear R-helpers: May I ask a question related to storing a number of lmer model fit into a list. Basically, I have a for-loop (see towards the bottom of this email) in the loop, I am very sure that the i-th model fit (i.e.,fit_i) is successfully generated and the character string (i.e., tmp_i) is created correctly. The problem stems from the following line in the for-loop #trouble making line

Dear R users, I hope this mailing list be the right place for my question. Usaully performance criterion of curve fitting like as Directinal Symmetry (DS) and Mean Absolute Percentage Error (MAPE) are correctly used with absolute time series which includes only positive values. But how it is possible to use DS and MAPE with other time series like as return series (for example return of a

CRAN (and crantastic) updates this week New packages ------------ * bisectr (0.0.2) Maintainer: Winston Chang Author(s): Winston Chang <winston at stdout.org> License: GPL-2 http://crantastic.org/packages/bisectr Tools to find bad commits with git bisect * CUMP (1.0) Maintainer: Xuan Liu Author(s): Xuan Liu <liuxuan at bu.edu> and Qiong Yang <qyang at bu.edu>

Instead of Tsp = c(2016, 2018, 12) try Tsp = c(2016, 2018.25, 12) Hence, you can specify the object as structure(c(5973156.76, 5159011.2, 6695766.64, 6365359, 6495218.53, 7226302.39, 6835272.7, 7383501.57, 6962748.19, 7623278.72, 7274994.33, 7919421.8, 7360740.81, 7436693.35, 8545765.55, 7337269.76, 8180585.44, 8376635.05, 7758261.24, 10374641.22, 8000314.11, 9114958.9, 9805149.15,

Dear R-experts, Doing cross-validation for 2 robust regressions (HBR and fast Tau). I can't get the 2 errors rates (RMSE and MAPE). The problem is to predict the response on the testing data. I get 2 error messages. Here below the reproducible (fictional example) R code. #install.packages("MLmetrics") # install.packages( "robustbase" ) # install.packages(

Hi. I am trying to follow this forecasting tutorial at: https://www.r-bloggers.com/basic-forecasting/ Using my own data, I cannot get past the first step, lots of laughs. dat3 <- structure(c(5973156.76, 5159011.20, 6695766.64, 6365359.00, 6495218.53, 7226302.39, 6835272.70, 7383501.57, 6962748.19, 7623278.72, 7274994.33 ,7919421.80, 7360740.81, 7436693.35,