Questions tagged [arma]

ARMA is an acronym for auto regressive moving average; a stochastic process modelling time series. It adds moving average terms to the AR model.

Overview

ARMA is an acronym for auto regressive moving average; a stochastic process modelling time series. It adds moving average terms to the AR model. Mathematically, a moving average model is specified by its order $q$. An order $q$ moving average model, MA(q) for a time series $Y_t$ is written as follows:

$$Y_t = \epsilon_t + \sum_{i=1}^q \theta_i \epsilon_{t-i}$$

Here $\epsilon_{t-i}$ ($i=0,1,2,\ldots q$) represent white noise, and $\theta_i$ ($i=1,2,\ldots q$) represent the parameters of the model.

An ARMA model is the combination of an AR(p) and MA(q) model. It is represented as ARMA(p,q), and takes the following mathematical form:

$$Y_t = c + \epsilon_t + \sum_{i=1}^p \phi_i Y_{t-i} + \sum_{i=1}^q \theta_i \epsilon_{t-i}$$

ARMA models are a special case of ARIMA models.

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Analyse ACF and PACF plots

I want to see if I am on the right track analysing my ACF and PACF plots: Background: (Reff: Philip Hans Franses, 1998) As both ACF and PACF show significant values, I assume that an ARMA-model will serve my needs The ACF can be used to estimate…
Peter Knutsen
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A proof for the stationarity of an AR(2)

Consider a mean-centred AR(2) process $$X_t=\phi_1X_{t-1}+\phi_2X_{t-2}+\epsilon_t$$ where $\epsilon_t$ is the standard white noise process. Just for sake of simplicity let me call $\phi_1=b$ and $\phi_{2}=a$. Focusing on the roots of the…
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What is the intuition of invertible process in time series?

I'm reading a book on time series and I started scratching my head in the following part: Could someone explain the intuition for me? I couldn't get it from this text. Why do we need the process to be invertible? What is the big picture here?…
jjepsuomi
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Whether a AR(P) process is stationary or not?

In practice, how to evaluate whether a AR(P) process is stationary or not? How to determine the order for the AR and MA model?
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Autocovariance of an ARMA(2,1) process - derivation of analytical model for $\gamma( k)$

I need to derive analytic expressions for the autocovariance function $\gamma\left(k\right)$ of an ARMA(2,1) process denoted by: $y_t=\phi_1y_{t-1}+\phi_2y_{t-2}+\theta_1\epsilon_{t-1}+\epsilon_t$ So, I know that: $\gamma\left(k\right) =…
hydrologist
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ARIMA vs ARMA on the differenced series

In R (2.15.2) I fitted once an ARIMA(3,1,3) on a time series and once an ARMA(3,3) on the once differenced timeseries. The fitted parameters differ, which I attributed to the fitting method in ARIMA. Also, fitting an ARIMA(3,0,3) on the same data…
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Different AIC definitions

From Wikipedia there is a definition of Akaike's Information Criterion (AIC) as $ AIC = 2k -2 \log L $, where $k$ is the number of parameters and $\log L$ is the log-likelihood of the model. However, our Econometrics notes at a well-respected…
pir
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Fitted values of ARMA model

I'm trying to understand how fitted values are calculated for ARMA(p,q) models. I've already found a question on here concerning fitted values of ARMA processes but haven't been able to make sense of it. If I have a ARMA(1,1) model, i. e. $$X_t =…
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Online material to learn time series analysis

My question is if there are any good online materials for learning this. Something that introduces things well, especially ARMA models and the related math. Edit: I'm looking for something of the high-end undergraduate level. Something like in…
Mr Alpha
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Why is forecasting of ARMA models performed by Kalman filter

What are the advantages of expressing an ARMA model as a state-space-model and do forecasting using a Kalman filter? This methodology is for example used in the SARIMAX implementation of…
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Prove expression for variance AR(1)

For the autoregressive AR(1) process $x_t = \delta + \phi x_{t-1} + \eta_t$, I am trying to prove that the variance is: $\sigma_x^2 = \sigma_\eta^2/(1-\phi^2)$ And that the first-order covariance is: $\gamma_{1,x} = \phi \sigma_x^2$. I have tried…
rbm
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Is there an equivalent of ARMA for rank correlation?

I am looking at extremely non linear data for which the ARMA/ARIMA models do not work well. Though, I see some autocorrelation, and I suspect to have better results for non linear autocorrelation. 1/ is there an equivalent of the PACF for rank…
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auto.arima does not recognize seasonal pattern

I have a daily weather data set, which has, unsurprisingly, very strong seasonal effect. I adapted an ARIMA model to this data set using the function auto.arima from forecast package. To my surprise the function does not apply any seasonal…
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Review of Box-Jenkins methodology

i just finished developing an ARMAX model with python (mostly statsmodels) in order to forecast some data. My next step is to test the data (24 time series) with the given ARMAX model. As i need to write a proper academic documentation about all…
Peter Knutsen
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Filtering using ARMA model in R

I have two time-series, x and y. I would like to prewhiten x by fitting an ARMA(p,q) (or in my case ARMA(1,1)) process and then use the coefficients to filter y. This seems like a pretty standard thing to want to do. However, the stats:::filter…
Alex
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