similar to: Generating output from auto.arima()

Hello everybody! I'm having this problem with the auto.arima function that i've not been able to solve. I use this function on time series that contains NA values, but every time that the resulting model contains drift I can't perform a forecasting (using forecast.Arima function). The printed error (when I try to forecast the resulting model) claims a dimension mismatch

I am using AUTO ARIMA for forecasting. But it is not detecting 'seasonality term' of its own for any data. Is there any other method by which we can detect seasonality and its frequency for any data? Is there any method through which seasonality and its frequency can be automatically detected from ACF plot? -- Siddharth Arun, 4th Year Undergraduate student Industrial Engineering and

I am using auto.arima() for forecasting.When I am using any in built data such as "AirPassangers" it is capturing seasonality. But, If I am entering data in any other format(in vector form or from an excel sheet) it is not detecting seasonality. Is there any specific format in which it detects seasonality or I am doing some thing wrong? Does data have to be entered in a specific

Hi, I am trying to fit an AR model, maximum order =4, order selection criterion is aic. I wonder why these two give different results: m1<-ar.ols(x, aic=TRUE, method="ols", order.max=4) m1<-auto.arima(x,d=0, D=0, max.p=4, max.P=0, max.q=0, max.Q=0, ic="aic") Could they both use the function predict to do forecasting? Is there any function that works better?

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

Hello, I have a question about function arima.sim I tried to somulate a AR(1) process, with no innovation, no error term. I used this code: library(forecast) e=rnorm(100,mean=0,sd=0) series=arima.sim(model=list(ar=0.75),n=100,innov=e)+20 Then I tried to applicate ti this series auto.arima function: mod1<-auto.arima(series,stepwise=FALSE,trace=TRUE,ic='aicc') The best model returned

Good morning everyone, I have attached an Excel file that contains a macro from which I call and use R's auto.arima function to generate forecasts. The program runs perfectly and it gets me the results; however, those results are pretty unusual. I also tried using the auto.arima function directly in the R console and still get weird results. The results are shown in columns AB, AC and AD

Hi, I am trying to forecast a model using predict.Arima I found arima model for a data set: x={x1,x2,x3,...,x(t)} arima_model = arima(x,order=c(1,0,1)) I am forecasting the next N lags using predict: arima_pred = predict(arima_model,n.ahead = N, se.fit=T) If I have one more point in my series, let's say x(t+1). I do not want to recalibrate themodel, I just want to forecast the next N-1

Here is my problem: Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc). Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc). arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation)

Hi, I've got a little problem using auto.arima. I run the following command auto.arima(drivers,ic="aic",d=1,D=1,max.order=10,max.p=5,max.q=5,max.P=5,max.Q=5,stepwise=FALSE,allowdrift=FALSE) and I get the following output : Series: drivers ARIMA(0,1,1)(5,1,1)[12] Coefficients: ma1 sar1 sar2 sar3 sar4 sar5 sma1 -0.6421

Dear all, I just have a short question regarding the forecasting of ARIMA models with external regressors. I tried to program a ARX(1) model arx.mod <- arima(reihe.lern, order = c(1, 0, 0), seasonal = list(order = c(0, 0, 0), period = 52), xreg = lern.design, include.mean = TRUE) for which I need to estimate the next (105th) value. Xreg=lern.design is - at this time - 104 rows long. I

I am trying to use auto.arima to fit a univariate time series and do forecast. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input RR; datalines; 12 14 17 15 13 15 15 14 15 14 16 15 15 18 16 16 15 14 15 16 16 14 13 12 ; run; I successfully took this data into R and used the auto.arima codes but am getting

Hello R users, Hope everyone is doing great. I have a dataset that is in .csv format and consists of two columns: one named Period (which contains dates in the format yyyy_mm) and goes from 1995_10 to 2007_09 and the second column named pcumsdry which is a volumetric measure and has been formatted as numeric without any commas or decimals. I imported the dataset as pauldataset and made use of

Hello, I am calling the auto.arima method in the forecast package at it returns what seems to be valid Arima output. But when I feed this output to 'predict' I get: Error in predict.Arima(catall.fit[[.index]], n.ahead = 12) : 'xreg' and 'newxreg' have different numbers of columns Is there a way to tell what is being supplied to xreg from the Arima output? Any ideas?

Hello, I using the fuction auto.arima() from package forecast to predict the values of p,d,q and P,D,Q. My problem is the execution time of this function, for example, a time series with 2323 values with seasonality to the week take over 8 hours to execute all the possibilities. I using a computer with Windows XP, a processor Intel Core2 Duo T7300 and 2Gb of RAM.

Hey, I have a few doubts with regard to the usage of the auto.arima function from the forecast package in R. *Background:* I have a set of about 50 time-series for which I would like to estimate the best autroregressive model. (I want to estimate the coefficients and order of p). Each of the series is non-stationary and are also have a non-normal distribution. The data is non-seasonal. My

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals),

Dear all, I would like to ask how my data set contains problems. Here is what I have done. SandP = read.csv("Price.csv") library("forecast") auto.arima(SandP) and R shewed this to me Error in model.frame.default(formula = x ~ 1, drop.unused.levels = TRUE) : invalid type (list) for variable 'x' In my data set SandP it contains 1 column of the daily

Dear R-helpers. Would appreciate if someone can explain the warning messages below, after auto.arima. I couldn't find any clue in the archived help. Also, how do I retrieve the AICs of each tried model in auto.arima? The purposes are (1) to output to a text file, and (2) to find the 2nd best model by finding 2nd lowest AIC instead of eyeballing thru the value at the console