similar to: auto.arima help

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

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

Colleagues I am a new to R but already love it. I have the following problem: I fitted arima model to my time series like this (please ignore modeling parameters as they are not important now): x = scan("C:/data.txt") x = ts(x, start=1, frequency=1) x.fit<-arima(x, order = c(1,0,0), seasonal = list(order=c(0,0,1))) Now I want to use this model for forecasting and backtesting (!).

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

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, 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

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

Hello everyone, I'm doing some benchmark comparing Arima [1] and SVR on time series data. I'm using an out-of-sample one-step-ahead prediction from Arima using the "fitted" method [2]. Do someone know how to have a two-steps-ahead forecast timeseries from Arima? Thanks, Matteo Bertini [1] http://robjhyndman.com/software/forecast [2] AirPassengers example on page 5

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),

Full_Name: Gang Liang Version: 1.7.1 OS: Debian/Woody Submission from: (NULL) (192.6.19.190) > print(arima.sim(list(ar=.3,order=c(1,1,1)), 30)) [1] 0.00000000 0.10734243 0.02907301 -1.23441659 -0.98819317 -2.82731975 [7] -2.69052512 -4.22884756 -5.02820635 -5.41514613 -6.20486350 -7.01040649 [13] -6.78121289 -5.41111810 -4.96338053 -5.42395408 -6.22741444 -5.75228153 [19] -6.07346580

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.

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

Greetings, I am trying to model a time series using arima(). For getting the model order components(p, d, q and P,D,Q) I am using procedure discussed in [1] in section 3.2 . It is most likely hit and trial method based on lower AIC value. I want to know what is the correct way to find model order components or the method described in [1] is the appropriate one. thanks in advance. -- [1]Automatic

Good afternoon! I'm trying to model a time series on the following data, which represent a monthly consumption of juices: >x<-scan() 1: 2859 3613 3930 5193 4523 3226 4280 3436 3235 3379 3517 6022 13: 4465 4604 5441 6575 6092 6607 6390 6150 6488 5912 6228 10196 25: 7612 7270 8617 9535 8449 8520 9148 8077 7824 7991 7660 12130 37: 9135 9512 9631 12642

Hello, I asked a question about what the most likely process to follow if after a time-series fit is performed the residuals are found to be non-normal. One peron responded and offered to help if I supplied a sample data set. Unfortunately now that I have a sample I have lost the emai addressl. If you are that person or have some ideas please email me back at rkevinburton at charter.net. Thank

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 am trying to run ADL model by using auto.arima in package "forecast". I put two time series,x and xreg, in the formula, but got message: Error in nsdiffs(xx) : Non seasonal data. Any one can tell how to use it? Thanks Richard *************************************************************************************************** IMPORTANT: * This transmission is intended for the

hi: I am trying to fit prediction intervals for an arima object. My search led me to the link: http://finzi.psych.upenn.edu/R/library/forecast/html/forecast.Arima.html which has the function "forecast", as I wanted. However, when I try to run it in R, I get the message: Error in plot(forecast(fit)) : could not find function "forecast" Even the example provided on the page

I'm trying to do the following out of sample regression with autoregressive terms and additional x variables: y(t+1)=const+B(L)*y(t)+C(1)*x_1(t)...+C(K)*x_K(t) where: B(L) = lag polynom. for AR terms C(1..K) = are the coeffs. on K exogenous variables that have only 1 lag Question 1: ----------- Suppose I use arima to fit the model: