Tagged Questions

Refers to the AutoRegressive Integrated Moving Average model used in time series modeling both for data description and for forecasting. This model generalizes the ARMA model by including a term for differencing, which is useful for removing trends and handling some types of non-stationarity.

474 views

Characteristics of gold prices in an ARIMA model analysis

I'm forecasting gold prices using an ARIMA model. An ARIMA model requires a stationary, non-seasonal, linear series. However, after reading a few books, it seems that gold price data is ...
249 views

MA terms in arima

With the arima function I found some nice results, however now I have trouble interpreting them for use outside R. I am currently struggling with the MA terms, here is a short example: ...
308 views

How to apply an AR(MA) model to a prewhitened signal?

I have two (vehicle velocity) signals that should consist of similar "latent" drivers, but have different autocorrelation structures. The driver-signals are quite nasty statistically, so I'm not ...
280 views

About P-value for ARIMA in R

I know I could obtain $p$-value by calculating coef/standard error using R command pnorm(). When the $p$-value of each coefficient is less than 0.05 (confidence ...
404 views

How auto.arima works?

I found auto.arima() function in forecast packages. As far I have understand how it works, It should find the best model for the ...
772 views

How can we detect trend in a time series apart from visual observation of time plots?

I'm novice in time series analysis and completely lost through reading so.. I have an enormous dataset of discrete time series of aggregated events and I wish to fit each one of them into ARIMA ...
814 views

SAS Proc Arima - DF, ADF, SIC, AIC, autocorrelation in residuals

I am using Proc Arima to produce the Dickey-Fuller and the augmented Dickey-Fuller tests. According to documentation Proc Arima uses the Dickey-Fuller method that tests the following hypothesis: ...
5k views

How to fit an ARIMAX-model with R?

I have four different time series of hourly measurements: The heat consumption inside a house The temperature outside the house The solar radiation The wind speed I want to be able to predict the ...
2k views

Fitting a multivariate ARIMA model with R

I have 4 correlated time series, and I want to predict one of them, from the other 3. There is a clear seasonal effect in the 4 time series, so my first thought was to fit a multivariate ARIMA model, ...
1k views

How is ARMA/ARIMA related to mixed effects modeling?

In time series analyses, I have used multi-level or random/mixed effects to deal with auto-correlation issues (i.e., observations are clustered within individuals over time) and added controls are ...
496 views

Proper ways to perform time series and ARIMA

Note that I do most of my analysis using R and Excel. Let's take this data set for example. I modified it as the data itself is proprietary: the years are also different: ...
520 views

Variable selection and forecasting with regARIMA models

I have a couple of questions about regARIMA models: What is the underlying principle of the R function auto.arima when xreg is different from NULL? Does it first ...
551 views

C# ARMA library

Do you know any C# library or source code that can be used for ARMA / ARIMA forecasting?
2k views

Problem with ARIMA in Minitab

I have made non-stationary data stationary by differencing at lag 1 and then differencing the differences at lag 11. The data values for months 1/06 through 6/11 are in an Excel file at ...
852 views

Seasonal data forecasting issues

I have a seasonally decomposed data set. The data set has strong seasonality. Now I am trying to fit the 'seasonal part' of dataset into ARIMA model and tried to forecast (with SPSS). The problem ...
5k views

Auto.arima with daily data: how to capture seasonality/periodicity?

I am fitting an ARIMA model on a daily time series. Data are collected daily from 02-01-2010 to 30-07-2011 and are about newspaper sales. Since a weekly pattern in sales can be found (the daily ...
5k views

Detrending method for nonstationary data

Could anybody help me with detrending data that is nonstationary? I have already made the mistake of trying to detrend it by plotting the residuals of a linear regression in excel but it was pointed ...
654 views

Box-Jenkins model selection

The Box-Jenkins model selection procedure in time series analysis begins by looking at the autocorrelation and partial autocorrelation functions of the series. These plots can suggest the appropriate ...
973 views

Forecasting stock prices time series based on independent factors using ARIMA model

I am trying to forecast time series of stock for a particular case in which closing value of the stock depends on independent factors which is in which infact another time series. Situation is like I ...
1k views

Model comparison between an ARIMA model and a regression model

I'm really having trouble finding out how to compare ARIMA and regression models. I understand how to evaluate ARIMA models against each other, and different types of regression models (ie: ...
324 views

Estimation of time series regression using GLS

I am trying to estimate time series model using gls method. The data is monthly from sep 1997 to april 2011 First I estimate the model and know that the erorr are IMA(1,1). For that I use the code ...
480 views

How to exploit periodicity to reduce noise of a signal?

100 periods have been collected from a 3 dimensional periodic signal. The wavelength slightly varies. The noise of the wavelength follows Gaussian distribution with zero mean. A good estimate of the ...
756 views

Fitting the moving average model

let v to be forecasted value for periods 1 through T and $v_{t}$ be its forecasted value at time $t$. We express $v_{t}$ as the sum of two terms, its mean at time $t$, and its deviation from the ...
1k views

How to update ARIMA forecast in R?

I have a time series data of 30 years and found that ARIMA(0,1,1) has best model among others. I have used the simulate.Arima (forecast package) function to simulate the series into the future. ...
386 views

Methods for evaluating partial autocorrelation for identification of ARIMA models

I am trying to programmatically identify an ARIMA model for a series of data and forecast values. Currently the problem i am facing is to find a way to evaluate partial autocorrelation. I have been ...
4k views

How to calculate the p-value of parameters for ARIMA model in R?

When doing time series research in R, I found that arima provides only the coefficient values and their standard errors of fitted model. However, I also want to ...
1k views

How can I calculate the R-squared of a regression with arima errors using R?

If I have an arima object like a: ...
2k views

Why are fitted.values not part the R object returned from arima?

Starting out with arima models in R, I do not understand why fitted.values (of an AR(2) process for example) are not part of the output like they are in regressions. Did I miss them when running ...
400 views

Aggregating results from Arima runs R

/edit: To clarify: The mtable function from the memisc package does exactly what I need, but unfortunately does not work with arima models. Similar to this question: I have multiple Arima models, ...
2k views

Simulation of ARIMA (1,1,0) series

I have fitted the ARIMA models to the original time series, and the best model is ARIMA(1,1,0). Now I want to simulate the series from that model. I wrote the simple AR(1) model, but I couldn't ...
2k views

Predicting daily electricity load - fitting time series

I want to predict inter-day electricity load. My data are electricity loads for 11 months, sampled in 30 minute intervals. I also got the weather-specific data from a meteorological station ...
2k views

Seeking certain type of ARIMA explanation

This may be hard to find, but I'd like to read a well-explained ARIMA example that uses minimal math extends the discussion beyond building a model into using that model to forecast specific cases ...
5k views

When to log transform a time series before fitting an ARIMA model

I have previously used forecast pro to forecast univariate time series, but am switching my workflow over to R. The forecast package for R contains a lot of useful functions, but one thing it doesn't ...