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

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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10 views

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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221 views

How to use error term in AR (2) model for predicting future values?

We use turbidity to estimate suspended-sediment concentration (SSC)- our data was serially correlated. We ran an ARMA process and ended up with a AR (2) model. Our equation in log form is: ...
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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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17 views

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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Difference between forecast and prediction?

I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mean ...
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45 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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80 views

Does dummy interention variable (pulse or step) must be differenced when it is added to ARIMA model?

I have read some opinions from this forum and from other sources that when the dependent variable in any from of ARIMA model (whether ARIMA errors, ARIMAX or transfer function)is differenced, you ...
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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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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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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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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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1answer
167 views

Multivariate Time Series Forecasting in R - data in 10 minute intervals

I have data where an observation was made in 10 minute intervals for 8 weeks. I have around 170 variables that were measured every 10 minutes. I am trying to use multivariate time series analysis to ...
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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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2answers
933 views

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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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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109 views

OLS versus ML estimation of VECM

A vector error correction (VECM) model has an equivalent vector autoregression (VAR) representation. (VECM) $\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta ...
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56 views

Estimation of VECM via ML and OLS

Take a vector error correction (VECM) model: $$\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta y_{t-(p-1)}+\varepsilon_t$$ where $\Pi=\alpha \beta'$ and each row of ...
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18 views

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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44 views

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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11 views

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
218 views

Finding structural breaks in heteroskedastic time series

I'm trying to identify structural breaks in the movement of reserve currencies. I'm not yet all that versed in the finer details of time series, but I've been reading up on ARCH and GARCH estimators. ...
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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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2answers
47 views

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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163 views

Data analysis: Time series for Bacterial population data

Could anyone please help with data analysis. Briefly, I am studying how the population of a bacteria changes at different points (port 1 to port 10) within a biofilter over the course of 30 days(like ...
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119 views

Markov Chain State Transition Probability in R

I have a dataset which shows the states (3 states) across 11 time points for each participant. I wanted to estimate the Markov Chain state transition probability matrices for time points 2-11 using R. ...
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236 views

Statistical comparison of two signals

I need to develop an algorithm that will compare two signals and generate some metric(s) to describe changes between them. Signal processing and analysis isn’t my strong point so I would appreciate ...
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1answer
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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14 views

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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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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2answers
269 views

Unit root test specification with a structural break

I am puzzled as to what specification I should include in my unit root tests of the following data: . I will use ADF, KPSS and ZA tests. I can see there is a break in trend at observation 9. However, ...
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313 views

Time series: ets() Box Cox transformation and AICc comparation

I am using ets() from the R forecast package and AICc criterion to select the best model. Suppose we have a time series denoted ...
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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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156 views

Finding significance levels for cointegrating coefficients in cajorls

I am investigating the long-term relationship of some variables using the R package vars, but in the output of the cajorls ...
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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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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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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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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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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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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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3answers
188 views

How to form a predictive model in R?

I have two data sets whose structure is like this: DATA SET 1: ...
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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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439 views

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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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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20 views

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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1answer
385 views

How to apply an AR(MA) model to a prewhitened signal?

I have two (vehicle velocity) signals that should consist of similar "latent" drivers, but have different autocorrelation structures. The driver-signals are quite nasty statistically, so I'm not ...