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

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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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5 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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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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12 views

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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10 views

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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1answer
25 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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1answer
17 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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42 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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7 views

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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10 views

How to build the A MAtrix for a A-Model(Amat) after a REDUCED VAR in R

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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52 views
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344 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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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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7 views

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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1answer
16 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
27 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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12 views

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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0answers
18 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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2answers
38 views

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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2answers
46 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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16 views

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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26 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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5 views

how to implement link anomaly method for discovering emerging topics [on hold]

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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2answers
146 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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1answer
44 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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4 views

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
32 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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31 views

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 ...
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38 views

Prediction based on multiple time series - Python

I have 3 predictors and 1 variable that represents ground truth. They all are linked time series. My purpose is with the 3 predictors to try to forecast the ground truth data. For example : ...
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28 views

How to compare and cluster sets of daily time series?

I have multiple dataframes each representing traffic speed for each day of the year (366 dataframes for 366 days of the year). The raws of the dataframe are timestamp from 00:00 to 23:55 at 5 minute ...
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2answers
112 views

Analyse ACF and PACF plots

I want to see if I am on the right track analysing my ACF and PACF plots: Background: (Reff: Philip Hans Franses, 1998) As both ACF and PACF show significant values, I assume that an ARMA-model ...
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1answer
40 views

Bootstrapping at group level (time series data)

I have time series of continuous measurements of two different variables $x(t)$ and $y(t)$ measured at times $t_i$. I measured those variables for different subjects (with different characteristics) ...
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2answers
68 views

What's the model representation for the first difference of a local level model?

This is my first exercise for space state models and I've a few questions I'd need to resolve before I actually start doing the exercise. Unfortunately, I'm self teaching (I have no professor to ask) ...
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0answers
17 views

How to evaluate a Bayesian forecast?

Suppose that I have a predictive posterior, which is an attempt to predict some one-step ahead forecasted value $\hat{y}_{T+1}$. How do I assess if my posterior has done a good job or not? If we had ...
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28 views

How to handle autocorrelated residuals (and independent variables) in multiple regression? [closed]

I am trying to carry out multiple regression for an air pollutant (dependent variable), and weather parameters- wind direction, wind strength, rain, air temperature, relative humidity (independent ...
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20 views

Comparison of Non-Stationary Time Series Trends

I am trying to compare two readings of the same occurrences from two different sources, forming two time series. I would like to assign a metric to their similarity/dissimilarity, but the method I am ...
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23 views

Suitable model for predicting mean output over time

I have 3 years of yearly historic data on the results of a harvest (of truffles) from multiple different areas involving multiple individuals harvesting in each area. The dataset contains: name of ...
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1answer
52 views

Is it possible to simplify linear regression if one of the variables does not have errors and is equispaced?

Specifically, I have a time series with fixed $\Delta t$ of the following form: $x_0, x_1, x_2, ... x_n$ and $t_0, t_1, t_2, ... t_n$ where $t_{i+1}-t_i =\Delta t $. I'm interested in the improved ...
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30 views

Maximum value of d in ARIMA model

I am trying to model a data series using ARIMA model. The series seems non stationary because the acf decays very gradually.Even after differencing two times, the values of p and q are coming as high ...
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24 views

Regressing nonstationary on stationary variable

I am trying to empirically estimate the coefficient for the Okun's law as a relationship between output growth and unemployment. I am using the simple gap version, where I regress real output growth ...
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26 views

Get groups in time series with categorical data in R for use in gts

I have sales data organised in a table with 6 columns (4 for the location and type data, and 2 for the dates and the quantity sold), and 24 rows for each category representing the sales over 24 months ...
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2answers
82 views

AR(q) model with F-test

I am wondering that if we have an AR($q$) model for time series: $$X_i=\beta_1X_{i-1}+..+\beta_{p}X_{i-p} + \beta_{p+1} X_{i-p-1} +...+\beta_{q} X_{i-q}+\epsilon_i,\epsilon_i \;\text{iid}\; ...
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11 views

KFAS example won't run [migrated]

I tried to get the example on page 13 of the KFAS manual to run but no luck. Anyone have any ideas? ...
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1answer
58 views

Statistically Approximating Clicks From Limited Data

Assume a business started in January 2014. I have the following daily data (from June 2014 to December 2014): 1. Number of people who joined the website; 2. Number of people who left the website; ...
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1answer
25 views

How to remove level shifts and pulses from time series?

I am interested in describing seasonal patterns in several time series and then seeing if they are related. My approach is to fit regression models with an indicator variable for each season which ...
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14 views

MANOVA on autocorrelated variables

I am trying to find out if signals 1 and 2, can explain signals 3 to 10. All signals are continuous and time-varying and are rather strongly autocorrelated. Signals 3 to 10 (my dependent variables) ...
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1answer
35 views

Using monthly product usage data to predict customer churn

I've been reading tons of papers detailing methods on predicting customer attrition, but none of them have mentioned using product usage data over time. We keep detailed logs of how many times User A ...
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1answer
39 views

What is the auto-covarriance of a stationary AR1 process?

Say a stationary AR(1) process is given by: $$ X_t = c + \phi X_{t-1} + \epsilon_t $$ where $ \epsilon_t $ is a white noise process with zero mean and constant variance $ \sigma^2 $. Wikipedia ...
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1answer
69 views

Finding occurrences of specific patterns in time series

I have to locate occurrences of Cyllinder, Bell and Funnel patterns in univariate time series $X$ of gamma-ray sensoring. This is a specific case of the general CBF synthetic problem found in a few ...
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Generating randomized time series from discontinues historical data

I have some historical data from microbiological sampling, which are not collected continuously. For example historical data are in 2008/01/12, 2008/03/25, 2008/5/30, 2009/07/05, 2009/07/29.For ...