**1.883 arima questions.**

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 ...

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 ...

In my studies I've been working recently on dependency between debt and GDP growth in USA from 1966 to 2015. I used logged and differenciated GDP time series data and combined it with 0/1 debt-to-GDP ...

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 ...

I am trying to choose the correct ARIMA model.
To get a stationary series on which to plot the ACF and PACF on I've done the following transformations on my original series:
natural log
1st non-...

I see the term "exponential smoothing" model used a lot in different applications but I never understood what exactly it is. Is it just a MA(1) model? Or is it any moving average model, meaning it ...

I have done a training set to fit different ARIMA models and then a test set to assess their performance (with R). From what I understood, I can use the AICc to determine the best model by choosing ...

Assuming that model is correct, why does the residual series of an ARMA model resemble a white noise process?

Given the following univariate ARMA(p,q) process $$ (X_{t})_{t\in Z } $$
$$
X_{t}=\alpha_{0} + \alpha_{1}X_{t-1}+...+\alpha_{p}X_{t-p}+\theta_{1}\epsilon_{t-1}+...+\theta_{q}\epsilon_{t-q}+\epsilon_{...

I would like to fit an ARMAX model in R of the form that is mostly used in literature:
$$y_t = \beta_1 x_t+\cdots+ \beta_{k} x_{t_{k-1}}+ \phi_1 y_{t-1}+\cdots+\phi_p y_{t-p}+ \theta_1 z_{t-1}+\cdots ...

I have a time series of integer values $x \geqslant 0$. I would like to model it using, say, ARIMA, or Holt-Winters. How do I properly preprocess it for the task? I tried log-transform of $x' = x + ...

I am new in R language. I have a time series data in seconds (15 second interval) for the period of 72 hours as shown below. I am using auto.arima() function for ...

I would like to forecast the non-stationary time series, involving several crucial a-priori assumptions following from studying of instances of such series.
I've constructed time-averaged one-point ...

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 have monthly price data for a commodity from 2007 to 2017. You can find it in the following link:
https://drive.google.com/open?id=0BxRCOgKAL4itcUZlOExrUmVOanc
I need to forecast it using Seasonal ...

I have a number of time series' that I would like to run PCA and then K-Means clustering on in order to find groups of similar behaving variables.
To do this I am trying to first apply an ARMA-GARCH ...

I have a data set of weekly sales for a range of stores (all belonging to one company). I am trying to predict weekly/monthly use of several ingredients in the individual stores. The choice for what ...

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 ...

How to deal with conditional heteroskedasticity in ARIMA model?
ARCH test on ARIMA model indicates the presence of conditional heteroskedasticity and ARIMA forecasts are therefore incorrect.
Is ...

It is suggested to use auto.arima with xreg in regression with ARIMA errors. Especially when dealing with multiseasonality with ...

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 ...

When returns rt are modeled as a MA(1) process with GARCH(1,1) innovations. Then what are the the predicted returns rT+1 and rT+2? And what are the prediction errors for those periods in time?

I am working with auto.arima. If I got this correctly, auto.arima depends on a ndiffs ...

I have to make a one-step ahead forecast for a time series Y(t) using R. Theory suggests the ideal model should be:
Y(t) = αX + βY$_{t-1}$ - βY$_{t-2}$
However, I don't know how to deal with the ...

I have a daily return time series, which is stationary(proofed by ADF test) , has no autocorrelation up to lag10(proofed by lbq test with lag10) and has ARCH effect(proofed by LM test). My initial ...

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

I am trying to fit an ARMA model to a time series of a power spectral density values that I have calculated.
Here is the plot of the data:
with corresponding autocorrelation and partial ...

I am using ARMA-GARCH on a financial time series to try and model and thus remove any autocorrelation and heteroscedasticity leaving me with a timeseries of residuals that is stationary.
I have ...

I am trying to write an application which impute some missing values on one time series signal. I have done it similarly in R using ImputeTS package but now need to do it similarly in Java.
I just ...

True/False Statements, if false make them true:
1.The model Yt = Yt-1
+ ut, where ut is a white noise process, is a process whose logarithm is stationary.
My idea: So the model itself is not ...

I have daily sales data for a department store for the past 850 days. I have indicators on the major holidays and the days leading up to the major holidays. The number of days before the holidays that ...

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 ...

I'm currently having the hardest time to understand (S)ARIMA model. Here are some information about my data:
My data goes up and down regularly and can take both negative and positive values.
Using ...

I'm having problems creating a reliable model to forecast this time series (quarterly data):
...

I have obtained these two plots using R, I have to fit a model and the trouble is choosing between an ARMA(0,0), and an AR3. The main issue is the autocorrelation at lag 3, is it enough significant to ...

An ARIMA model is specified by 3 parameters $(p,q,d)$ or 6 (+1 for the seasonality) if we consider a seasonal ARIMA model $(p,q,d)(P,Q,D)_s$.
The AIC used to select ARIMA models is calculated by:
$...

I have 3000 customers in my base and i want to forecast next 6 months revenue for each of these 3000 customers. Does that mean i have to build 3000 arima models 1 for each customer?
I can build a ...

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 ...

We’ve run Arimax models in R using the auto.arima function. Following was the output
Coefficients:
Birth_Rate_Change Proportion_Female_labour Females_20_39
97.7658 ...

I am working with a time series on monthly base (April 2004 - Oct 2016) in order to identify an ARIMA model and do forecasting.
This is the time series I examine:
month;volume
Apr 04;2.555
Mai 04;2....

Following this post How to use auto.arima to impute missing values, and the really comprehensive answers there:
Is it possible to implement this gap filling method with covariates, e.g. using climatic ...

I have a time series I am trying to forecast, for which I have used the seasonal ARIMA(0,0,0)(0,1,0)[12] model (=fit2). It is different from what R suggested with auto.arima (R calculated ARIMA(0,1,1)(...

In econometric literature it is usually argued that in case of estimating an equation, an intercept
term must be always included regardless of its statistical importance because removing the constant ...

I have a problem with my ARIMA(1,1,1) predictions. I have a time series with no seasonal component but with an obvious trend. To get rid of it I take the first difference by setting d=1. The model ...

I'm trying to solve a assignment from an university statistic course regarding the ARIMA models using matlab.
The assignment is:
I have:
an Y vector of size n of data
an NxK matrix with a trend and ...

I'm working through this tutorial and this guy run SARIMAX model for a time series with both seasonal and trend components:
...

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 ...

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

I have a SARIMA model with outliers effect:
...

I'm doing some work in time series analysis. I'm decomposing a time-series of a moving average as follows
...

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