Questions tagged [box-jenkins]
The Box-Jenkins procedure is used to identify the orders of an ARIMA model to apply to a time series.
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Using Box-Jenkins methodology for intervention analysis
I'm trying to follow B-J methodology for my intervention analysis. My understanding of of the first step is that, one should detrend any systematic trends such as seasonality then determine the lag ...
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How to identify ARMA order of Panel Regression
I am familiar with methods to determine the Autoregressive and Move Average orders of ARIMAX type models in a univariate time-series context, usually formulated as regression with ARMA errors. I am ...
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Recursive Maximum Likelihood Estimation algorithm - The same as Maximum Likelihood Estimation?
I have the book "Adaptive Control", Second edition, from Karl-Johan Åström and at page 61 to 62 he wrote:
Stochastic models
The least-squares estimate is biased when it used on data ...
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How to estimate parameters of a nonlinear polynomial model by using Maximum Likelihood Estimation?
Assume that you are having an equation that looks like this. It's a Box-Jenkins model.
$$F(q)D(q)y[k] = B(q)D(q)u[k] + F(q)C(q)e[k]$$
Where $F(q), D(q), B(q), C(q)$ are polynomials such as:
$$B(q) = 1 ...
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When is it appropriate to forecast data which were transformed with the log(diff) function?
For a work, we have to apply a Box-Jenkins approach to a certain data. We choose to study the total industrial production in Belgium (monthly data). As we have to forecast the data, we did multiple ...
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AR Modelling. Box Cox and Differencing don't give Stationary Data
I am trying to fit an AR/ARIMA model on electricity data on hourly prices during a almost three year period, I am following the guidelines in Hyndman, R.J., & Athanasopoulos, G. (2018) to do this. ...
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Selecting ARIMA orders by ACF/PACF vs. by information criteria
We keep on getting questions here about selecting ARIMA model orders based on ACF/PACF plots. This is the older methodology proposed by Box and Jenkins.
More modern tools like the ...
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Arima model not showing seasonality in its forecast
The following is a seasonal(not perfectly) time series sequence that I am trying to fit an ARIMA model to:
I performed box-cox transformation, 1 seasonal differencing and 1 regular differencing to ...
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Inverse differencing and inverse box cox on forecasted arima predictions
I am working on a time series project with non-seasonal data which has a non-constant variance. So in order to solve that issue I used box cox transformation to get the data in a suitable format,
<...
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Negative forecast value using ARIMA from fable and NaNs warning using ARIMA
My data set is a weekly data that contains two variables Production and Shipment. Production is the independent variable and Shipment is the dependent variable. First I'm trying to forecast Production ...
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Is non-invertibility a problem for (AR)MA processes?
I'm reading Time Series Analysis: Forecasting and Control (3rd ed.) by Box, Jenkins and Reinsel.
There are some arguments about invertibility that I can't wrap my head around.
Considering a MA(1) ...
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Box-Jenkins Methodology: Statistical model checking
According to https://en.wikipedia.org/wiki/Box%E2%80%93Jenkins_method :
Statistical model checking by testing whether the estimated model conforms to the specifications of a stationary univariate ...
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Box-Jenkins methodology in a Bayesian framework
I would like to know if the well-known Box-Jenkins methodology for time series analysis transfer directly in a Bayesian framework.
For instance, does one analyze the autocorrelation plot and the ...
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Parameter estimation of ARIMA model with exogenous variables (ARIMAX)
I am trying to compare an ARIMA model based on the price of a cryptocurrency without exogenous variables to one which adds in the number of tweets about the crypto in the same period as an exogenous ...
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Searching for Clive Granger's 1972 paper on time series decomposition from the Budapest Conference
In 1972 Clive Granger presented a seminal work on the Box-Jenkins Time Series methods as a conference paper:
C. W. J. GRANGER, Time Series Modelling and Interpretation, Paper presented to the European ...
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Autocorrelation (acf) and Partial Autocorrelation (pacf) to identify Box-Jenkins models
Can you help me to identify which Box-Jenkins models in these pictures?
I have read behavior ACF and PACF to identify whether this is AR, MA, ARMA or ARIMA but I'm so confused because none of the ...
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calculating truncated infinite AR weights in practice for Arima model
I am trying to figure out how to form the truncated infinite AR weights for a general time series process.
$(1 - \phi_1 B - \phi_2 B^2 - ... - \phi_p B^p)(1 - B)z_t = (1-\theta_1 B - ... - \theta_q ...
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What is the proper ARIMA model for the data?
I'm doing a project about inflation forecasting. I do the seasonal differencing of the log of CPI (log(cpi)-log(cpi(-)). After that series still exhibit the nonstationary nature, so I need to ...
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Over-fitting SARIMA model
I am currently running an iterative process(for loop) to determine best ARIMA model for monthly sales data according to smallest AIC and MAPE.
Box-Jenkins methodology clearly states to choose the ...
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Selecting ARIMA orders based on ACF-PACF vs. auto.arima
I use R to fit an ARIMA model to a time series (yearly granularity):
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Is Box-Jenkins approach to time-series prediction and forecasting similar to Unobserved Components models approach?
How I understand the Box-Jenkins Method in a nut-shell is that a time-series model has signals that can be identified by weighting its own past lagged values, or weighting its owned past errors or ...
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Are Box-Cox and differencing redundant or complementary?
I was always under the impression that differencing and Box-Cox were two ways to achieve the same goal: Making a time series stationary so that it can be modeled using an ARMA process.
However, ...
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The theory behind fitting an ARIMAX model
I'm very familiar with the theoretical underpinnings of ARIMA/SARIMA models but I've been struggling to understand the theory behind fitting an ARIMAX model. I'm not looking for a practical ...
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How to interpret these acf and pacf plots?
I don't know which model to fit to these ACF and PACF. Is it an AR(3) or something else?
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Why would you introduce a constant in a moving average model, when you already have the option of differencing?
In the online forecasting book of Hyndman (https://otexts.com/fpp2/MA.html) firstly the use of differencing is explained. After that he shows the formula for a moving average model:
$$y_t = c + \...
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Parsimony and Box Jenkins
Suppose that you want to estimate volatility of stock returns with the arch/garch family. An important step is to estimate the mean equation.
Suppose that you estimated e.g. an ARMA(5,4) model for ...
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Problem of extremly increasing Partial Autocorrelations in time series data
I'm currently trying to make a forecast of the use of prepaid payment
instruments using ARIMA modelling in Stata. I have a time series data set, containing monthly oberservations from April 2011 to ...
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Parsimony in Box -Jenkins model
Book of Enders says:
A parsimonious model fits the data well without incorporating any needless coefficients.
I am confused with this tradeoff, coefficients ...
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What can cause autocorrelation in higher lag orders of returns?
I am fitting an AR(p) model to the daily time series of S&P500 returns. I have examined AIC/BIC up to 5 lags and both show that model with 2 lags is optimal. However, when I examine the residuals ...
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I have correlogram ACF and PACF below for a temperature time series. Can I say it is MA(2) from ACF? What about AR?
ACF and PACF for monthly average temperature time series:
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Interpretation of this ACF plot
Currently I am trying to determine whether a sample of monthly returns can be seen as the outcome of a random sample. I plotted the ACF for 20 lags and got the following plot:
I am uncertain by the ...
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Could somebody help me read these ACF and PACF plots?
So, I have this time series that I have already forecasted using an ARMA model, but I am new to this and am therefore not at all sure whether or not I did this (somewhat) correctly.
I got the best ...
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Issues on analyzing a time series, ACF and PACF quite the same
I'm attending a econometric course and I'm new to time series analysis. They gave me a time series to analyze and I'm trying to apply all the things I learnt (and understood) to an actual time series.
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ARIMA model with splitted data
Question: When analyzing a time series of size $N$ assume that you fit models for data in the intervals $[1,N/2]$, $[N/2+1,N]$,
$[1,(2/3)N]$ and $[(2/3)N+1,N]$. Discuss what this approach is for.
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ARIMA Analysis (Box Jenkins Method) In R
So I have a time series which I cannot share with you all, but I have a few questions about the proper proceedings to fit the correct ARIMA model for my data.
I have successfully written a loop to ...
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ACF and PACF in Box Jenkins
guys!
I know this might be a silly question, but I am really curios if my interpretation is correct. I have some stock returns and try to fit and ARMA model I took the log difference and I want now ...
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auto.arima() fits a different model than ACF/PACF plots suggest
I have a data set that is transformed to stationarity and I'm trying to fit it to an ARIMA model. I found that variance is lowest when the transformed set is differenced to 1, and here are my ACF and ...
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Building an ARIMA model using ACF, PACF, etc
I wish to better understand the details of ARIMA models and how to interpret ACF and PACF graphs in determining what type of ARIMA model to use.
From my studies so far, I understand that there are ...
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Check the stationarity of a time series [duplicate]
I've a time series, 100 observations. I'm following the the Box-Jenkins method in order to find a model that fit the data. My questions (I'm a bit confused):
if I plot in a graph time vs observation ...
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ARIMA Box-Jenkins analysis with no significant ACF/PACF lags
I am analysing the stock index returns data for few countries. From observation of the ACF and PACF there seem to be no significant peaks at any lags.
However, applying the auto.arima function from ...
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What is the ARIMA model for this data?
I have made my series stationary by using one difference and have plotted the following acf & pacf:
So I have decided to test the following models:
• ARIMA(0,1,1) since the acf cuts off after ...
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Box-Jenkins to prove model
Suppose the $X_t$ satisfies:
$$\phi_1 (B) X_t = \theta_1 (B) Z_t, \tag{$*$}$$
where $\{Z_t\},$ $WN(0, \sigma_Z^2)$ and $\phi_1 (\cdot)$ and $\theta_1 (\cdot)$ are polynomials of order $p_1$ and $q_1$...
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Fitting a Multivariate ARIMA Model (in R) [closed]
I've been working on a high school project attempting to determine whether or not there exists a relationship (and if it exists, information on the strength and duration of the relationship) between ...
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Find state space model to compare with Box-Jenkins ARIMA model
I asked a question here about how to get predictions for the random-walk component of an ARIMA model.
Are there time series models in the state-space framework that might be suitable for the kind of ...
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Determining order of ARIMA model using Box-Jenkins. Correct approach / argumentation?
I obtained a couple of time series from estimating my (mortality-)model which I now aim to forecast with an appropriate ARIMA(p,d,q) model, which should be chosen with the use of the Box-Jenkins ...
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Box-Jenkins Forecasting With ARIMA(p,d,q) models
I want to check that I understand the general theme of forecasting with ARIMA models using box-jenkins, so I am going to take an example and then proceed from there.
We will use $B$ notation for the ...
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ARIMA Specification from Correlogram
How should I determine the data generating process from the correlogram below? This is non-seasonally adjusted monthly data that has been 1st differenced. I am trying to conduct univariate time ...
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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 ...
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What exactly is the Box-Jenkins method for ARIMA processes?
The Wikipedia page says that Box-Jenkins is a method of fitting an ARIMA model to a time series. Now, if I want to fit an ARIMA model to a time series, I will open up SAS, call ...
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