Time series are data observed over time (either in continuous time or at discrete time periods).

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Averaging time series to improve stationarity - loss of power?

Short version: When averaging over a presumed stationary time series and calculating statistics (e. g. normalized mean square error) to compare to a simulation (atmospheric turbulence model) of the ...
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Autocorrelation function in R for observational data

I'm trying to replicate an analysis done in Stata with R that involves calculating the autocorrelation for a particular outcome measured in many different areas. I've already run a linear regression ...
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Detecting anomalous bivariate timeseries in a time series database

Imagine that you have a time series DB consisting of measures of a repeated process and you want to find all those series which seem to be anomalous. An exemplary bivariate time series is depicted ...
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modeling time series data with lm()

After you decompose a univariate time series with stl() function in R you are left with the trend, seasonal and random components of the time series. Is it valid to use those components to then model ...
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Guidelines to obain the MLE from the definition of a function

The output of a linear model $x_t = s_t(\theta_0)+ n_t$ where $n_t$ is Additive White Gaussian noise of zero mean (AWGN), $\theta_0$ are the set of unknwon parameters of $s_t$ and $s_t$ is the signal ...
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trouble plotting ARIMA [on hold]

I'm having trouble plotting a basic ARIMA model. I understand I can do a plot(fitted(modelname)), but the book I'm working through says the command herein is possible. The book actually suggests ...
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12 views

comparison behavior of time series [on hold]

I have three sets of time series data and I want to compare compare pro-all with two others. I want to validate our simulation output trend (c=0.1 and c=0.2) with real word trend (pro-all). I use ...
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Who first suggested to approximate phases from a time series via marker events?

A rather simple approach to approximating an instantaneous (unwrapped) phase $φ$ from a time series is as follows: Define some a appropriate marker events (e.g., upwards zero crossings) $t_0 < … ...
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46 views

ARIMA predictions constant

I've created an Arima model based on past forex closing prices using auto arima, which has generated a (0,1,0) ARIMA model. ...
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1answer
43 views

Distinguish an ARMA and an ARIMA model graphically

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 ...
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8 views

To sketch a “typical” plot of a specific time series model

Let X have a distribution with mean $\mu$ and variance $\sigma^2$, and let $Y_t = X$ for all t. Sketch a “typical” time plot of $Y_t$. My thoughts: This process $Y_t$ is stationary with mean $\mu$, ...
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Clarification on ARDL/Unrestricted Error Correction Model

I have a few questions about unrestricted error correction models. The UECM for a model where $Y$ is the dependent variable and $x$ is the sole independent variable is given by, $$ \Delta ...
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Prediction using categorical, binary and time series variables

I have per subject: categorical variables - ex: grade, mother_education continuous variables - ex: ...
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24 views

Optimizing a time-series with multiple predictors

I have a few questions about turing a univariate time series into a multivariate time series and optimizing the predictors. Here is the univariate data: ...
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1answer
24 views

Panel data: Compare two different assessment methods

Just wondering if anyone has any inventive (but relatively simple) ideas about how to approach comparing panel data from two different assessment methods that collect the same variables. Basically, I ...
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8 views

Sensitivity Analysis of MA Process

Can sensitivity analysis be carried out for a time series moving average process? In a time series process, we wish to shock the x variables and beta coefficients and observe the effect on y. But in ...
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Why are “time series” called such?

Why are “time series” called such? Series means sum of a sequence. Why is it time Series, not time sequence? Is time the independent variable?
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Filtering time periods where relationship is swiched on/off

Sorry for the "unmathematical" formulation of the problem to come, but I am not sure where to place my problem: Suppose there exists a relationship between the variables x and y. I can observe both ...
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18 views

Bayesian Time Series Analysis Source

Is anyone able to recommend a source that covers Bayesian time series analysis in Winbugs?
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26 views

How do panel regression estimates differ from those obtained from multiple time series regressions?

I am trying to familiarise myself with panel regression techniques and I would like to know how the parameter estimates obtained from a panel regression model differ from those obtained from multiple ...
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1answer
30 views

Forecasting in Stata

I am working with time series data and fitting an autoregressive model using OLS. For reference, here is my price data for the commodity (I am not sure how to better format data for this site): ...
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Dependent variable is non-stationary and independent variable is stationary - residual series?

I ran a regression model where dependent variable is non-stationary (I know this is wrong) and my independent variable is stationary...I find that the residual series are stationary... how is it ...
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1answer
15 views

How to make series stationary when dependent variable is log(y)

I need some help in understanding the following: I have a time series data (y) that I am using to run regression models. However, my dependent variable is log(y). Should I test for stationarity of ...
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1answer
22 views

Is this a job for mixture of experts regression or semi-hidden markov models or something else?

Data I have several thousand timeseries each comprising around 365 data points. Browsing through a few of them, it looks like each timeseries consists of several regimes (different number f regimes ...
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Base sales in multivariate time series | MCMC model

I have been looking around online for good resources that explain how one would go about calculating base sales when preforming marketing mix modeling. I was told by a colleague that essentially they ...
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Model selection and performance evaluation using cross-validation for time series with missing values

So my task is to select and evaluate a statistical model (random forest, boosted trees, neural networks etc.) for a time series with missing values around 10 years long. One of the goals of that is to ...
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32 views

why the non-seasonal and seasonal parts are multiplied in ARIMA models?

I would like to understand why the non-seasonal and seasonal parts are multiplied in Seasonal ARIMA models. To be more specific: when we use the Seasonal ARIMA model we assume a multiplicative ...
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Comparing data that has been recorded on two devices

I have two data loggers which are recording a physiological signal. Device A is a system that has been in place for many years, and records data ~once/minute. Device B is a prototype device which ...
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69 views

Daily Ticket Sales

I looked around to see if there was a similar question, but couldn´t find one. I apologize if there is one and I missed it. I have the amount of ticket sales per day for 10 different events. The ...
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Can you specify AR(p) structure for cyclic spline in mboost?

Suppose I fit want to fit a boosted GAM using mboost:gamboost to time series data. Is it possible to specify an AR(p) structure for the cyclic component following a ...
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How to build the A MAtrix for a A-Model(Amat) after a REDUCED VAR in R [migrated]

i have a doubt in how to build the A MAtrix(Amat) for Estimating a SVAR model in R: I estimated a reduced VAR with the GDP, interest rate and inflation variables . With the economic theory and the ...
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Best method for short time-series

I have a question related to modeling short time-series. It is not a question if to model them, but how. What method would you recommend for modeling (very) short time-series (say of length $T \leq ...
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13 views

Correct df in longitudinal linear mixed model?

I am having trouble understanding how to correctly apply a linear mixed model to my data to measure the effect of wifi exposure. 4 beehives contained sensors collecting data on temperature (DHT22_t, ...
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1answer
58 views

Nonlinear Autoregressive model parameter estimation from time series

I'm working on a nonlinear multivariate autoregressive model of order 1 (markovian). It is a discrete-time dynamical system which models exchange of mass between compartments in a compartmental model ...
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An observation too short / missing data in panel

I have a panel data set with 7 lines or concepts from 1948 to 2013. However there is an 8th concept that I need that is only from 1993-2010. Is there a way in which I could estimate this variable's ...
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28 views

How should I represent validity of a population prediction?

I am looking to report on the validity of a predictive non-linear population model for which I only have the output prediction p(t) and the time for which the ...
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1answer
31 views

Time series with multiple subjects and multiple variables in R

I'm having trouble finding a time series technique to deal with a data set I am working on. It contains multiple subjects and multiple variables, not all of which will likely be part of the time ...
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Forecast Error Variance Decomposition with restricted VAR model

For conducting Forecast Error Variance Decomposition (FEVD) on a restricted VAR model I use the fevd method in the package vars ...
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Obtaining adjusted proportions with logistic regression

Can I obtain adjusted proportions of a binary variable by using logistic regression? I have a binary variable (normal/abnormal), which I'd like to obtain adjusted prevalence for (i.e the proportion ...
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fitting a cubic polynomial to a trend component of time series

I have 295 observations of two variables, of which here are a few: ...
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52 views

How can I model a binary outcomes in time series using logistic regression?

My data has a binary outcome (attack or not attack), day (20 day in repeated measured design) and some covariates (nestling’s movement). The objectives of my experiment are testing the effect of time ...
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Joint distribution R [migrated]

I have a list of 10 stocks, with each having a time series of log returns (AIG, JPM,...). I have calculated the log returns for each of the stocks as follows: ...
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30 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
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how to implement link anomaly method for discovering emerging topics [closed]

We are trying to do a project to discover emerging topics in social network via link anomaly method. But we are not knowing how to implement this.
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152 views

Do you see trends in my residual plots?

Do you see trends in my residual plots? These residuals plot show the standardized residuals against fitted values, origin period, calendar period, and development period. The patterns in any ...
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48 views

Residual based bootstrap autoregressive series in MATLAB

I have defined the model as follows. Let $$y_1 = 0$$ and $$ y_i = \alpha + \beta y_{i-1} + \epsilon_i $$ for $i_2\ldots i_T$, where $\alpha$ and $\beta$ are the estimated coefficients and ...
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simulating AR(1) process for N variables in MATLAB [migrated]

I am quite new in programming, can somebody tell whether the following approach is correct in matlab? clc; clear; N = 15; T = 221; alpha = randn(N,1); % normally distributed intercept as ...
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1answer
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Help understanding how the cointegration equation for VECM models are derived

I am learning about Vector Error Correction Models from Sean Becketti's "Introduction to Time Series using Stata". While I know how to run the Stata commands to estimate the VECM, I have no idea why ...
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Using seasonality in a model

Suppose you are modeling sales of a prepackaged good and suppose there are seasonal periods in the time series. Also suppose that the brand of the prepackaged good you are modeling comprises the ...