**1.904 arima questions.**

I have physiological time-series data: ~60k observations per channel, ~100 Hz sampling. I will model individual channels with ~20 regressors.
Under OLS, given temporal autocorrelation in the data, ...

I am trying to learn about Dynamic Regression models. However, the sources on the topic is (relatively) few compared to other TS topics, and so I cannot really get a grasp of where to start. I really ...

Hello, I have a non-stationary time series (population data) with 66 observations. Attached png file contains the acf and pacf plot for differenced (d=2) series.
(1) From that I assumed p=1 & q=1 (...

I have two time series to work with, let's say $X_1$ and $X_2$.
First I have to estimate the best pure ARMA model for $X_1$; which is no problem. For that I perform the following steps:
Stationarize ...

Given below are the errors, dataset and code snippet. What do I need to do to run the model?
For CSS the error is
Error in solve.default(res$hessian * n.used) :
Lapack routine dgesv: system ...

Why is it giving me a straight line whereas we can see that there is a pattern? Please tell me what I am doing wrong. I have build this model in python using statsmodel library.
I want the forecast ...

I tried to compute parameters of ARIMA/GARCH in two step. The first one is to build ARIMA and then fit GARCH using iid Gaussian MLE estimation. The second one is to construct ARIMA/GARCH ...

I have a very noisy time series like this and I forcast future values with auto.arima from the forecast package in R:
...

I need to find the conditional variance of $Y_t$ given information up to time t.
$Y_t$ = $\mu $ + $\phi_1$$Y_{t-1}$ + $\phi_2$$Y_{t-2}$ +$\epsilon_{t+1}$
Need to find the conditional variance of ...

I am currently doing a project that employs intervention analysis. I understand that we are supposed to use the pre-intervention data to formulate a noise model. However, Walter Enders said in his ...

I am working on a problem of predicting event counts based on user history.
This is a classical time series analysis problem, and I used the ARIMA model: (wiki).
I also applied a Hawkes point process ...

English is not my first language so i apoligize for any mistakes.
I have been given a dataset containing around 700 observations of the amount of a certain chemical in the air. The observations are ...

I have written the following code to generate 500 data points from a $SARIMA$ model, use $400$ as training data and then predict the following $100$, while estimating the model with AIC. It appeared ...

I've read in a quarterly time series with one y variable and 16 x variables (potential features):
dput(y)
structure(c(683.705, 719.185, 702.629, 764.002, 700.136, 745.584,
709.971, 772.7, 700....

Which is best and why between 2 models A and B where :
Log likelihood of A < Log likelihood of B
AICC of A > AICC of B
Thanks for your replies

If a time series process depends on its own past values then it's a AR process. These is what i understood but if it depends on it's own error then it's a MA process. Here is where i get confused.
...

I have an hourly time series of the average parking occupancy with data available from September 2017 up until June 2018. I would like to use the ARIMA model with external regressors to produce a ...

I have read a statement in a lecture note that for an MA(1) model
$X_t = \theta \epsilon_{t-1} + \epsilon_t$ with $|\theta| < 1$, where $\epsilon_t$ are white noise variates:
We can forecast only ...

I am missing something. I am trying to estimate an ARMA(2,2) model using Maximum Likelihood estimation via the scipy.optimize.minimie function.
I have simulated an ARMA(2,2) process via the ...

It's been more than 2 years that I am working on different time series. I have read on many articles that ACF is used to identify order of MA term, and PACF for AR. There is a thumb rule that for MA, ...

I am attempting to write the mathematical model for and also simulate an MA(1) process that has drift (in R).
I have referenced ARIMA (0,1,1) or (0,1,0) - or something else?, Simulation of forecasted ...

I read some papers on the non-seasonal ARIMA model, and the consensus I've seen is that for ARIMA(p, d, q), p and q should not be greater than 3, maybe 5. What's the reasoning for that? Is it for ...

For a stationary $AR(p)$ model, $\theta(B)X_t = w_t$, how can I show that $\theta(z) \neq 0$ for all $|z| = 1$.
I tried it as:
$\theta(z) = (1-\frac{1}{\lambda_1}z)(1-\frac{1}{\lambda_2}z)...(1-\...

I am working on Malaria cases vs. Meteorological variables. I want to fit a SARIMAX model using met vars to predict cases. My query is how to find multicollinearity between them (Independent) to ...

Consider the MA(p) process
$ y_t = \theta_0 \varepsilon_t + \theta_1 \varepsilon_{t-1} + \ldots + + \theta_q \varepsilon_{t-q} $,
where $\theta_0 \neq 1$, contrary to the convention taken in most ...

I am using the forecast package and the auto.arima function.
This function tries different arima model with different p and q ...

I've got a little problem here. I've been doing analysis with time series data using ARMA, and it always turns out that the parameters I get from R didn't fit to my computation when I do it manually.
...

I tried my data into an $ARMA$ model which is turned out to be $ARMA(2,3)$. I want to extract the model from the parameters I got in the pic (without any transformation). Is it right if I write the ...

I have a database based on hourly data and I need to forecast next 24h of a single variable. I was thinking about applying an ARIMA model with some exogenous variables but I don't succeed to configure ...

I want to fit ARIMAX model in R. For simplicity, let's consider model: $Y_t = \theta Y_{t-1} + \beta X + \epsilon$.
I know function auto.arima(), but it fits ARIMA ...

I am just wondering that, in terms of the multi-seasonal time series forecast, what is the difference between
using auto.arima find the ARMA order, then fit ...

I'm trying to unravel the influences of various exogenous regressors on a time series dataset. I'm getting good results with a sarimax(1,0,0)(0,0,0,0) specification, but I'm confused about the ...

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 ...

I'm an actuary trying to model future losses. I have the ability to use monthly or quarterly data, and I'm curious if there is any material difference in using either periods. I understand there may ...

Say for an ARIMA function with orders, ARIMA(3,1,1). How does ARIMA know which lag to consider. It may not be always be lags at t-1, t-2, t-3. It could be t-4, t-15,t-22.

This question is addressed to expert in econometrics. I generally fit econometric models and statistical learning models to financial time series and some discretionary traders usually asked me if I ...

I am working in a online supermakert , my current work is to predict daily sale count of fresh goods.
I tried to use time series model ARMA and xgboost, but both didn't fit well.
The problem is ...

I have daily visitors data for the last 10 years. I want to do some basic tests like which is the busiest day, which is the busiest month, busiest week etc. I used ...

I only have a very basic understanding of time series analysis. As I am learning ARIMA and then ARCH/GARCH models, I have some subtle (at least for me) questions on the common procedure to build such ...

My question is around the conceptual difference between Holt-Winters and ARIMA.
As far as I understand, Holt-Winters is a special case of ARIMA. But when is one algorithm preferred over the other? ...

In the $Y_t\sim ARMA(p,q)$ model, when the errors have Normal distribution, the unconditional distribution of $Y_t$ is Normal. When the errors have a
t-student distribution with $\nu$ degrees of ...

Would it be correct to say that the series is stationary in the below code, since only ARMA order is specified?
...

I used this tutorial to find optimal coefficients for my ARIMA model and still it pretty bad (see picture). How can I improve it?
...

Of the 60 series in my dataset, 26 don't exhibit an ARCH effect. I have first fitted an ARIMA model (auto.arima() in R) and tested it's squared residuals for ...

I'm currently analyzing some time series data and I need to know how to distinguish an ARMA model from an ARIMA model just by looking at the auto-correlation function and partial auto-correlation ...

Is my understanding off with what to expect from the following functions output:
...

I have a project about time series analysis. My data are not stationary and they have daily seasonality as shown in figure below. Is it correct to do the following steps?
Decompose Time serie into ...

I have very little knowledge of time-series analysis (despite my stat master - didn't do anything else than an introductory course) but now I'm facing a statistical problem whose answer is this very ...

Suppose I am using an ARIMA model to predict monthly sales in my business.
Now my data has some seasonality month on month and overall a trend upwards.
I use some mathematical tools to make the data ...

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