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

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Applying ARMAX model from r output

I'm trying to apply R output to generate a scenario using external data, I'm not sure how exactly to use the coefficients in each from the R output. I have an ARMAX(1, 1) model Coefficient of AR1: ...
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19 views

Prediction using CRFs for time series data

I have a little confusion about validity of some predictions I am making using a CRF model I have trained. The CRF model is trained on some input time-series, and when making predictions, I am ...
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25 views

VAR and Granger causality test

Is it necessary to calculate VAR before Granger causality test so that we can have the lag length to be used in Granger causality test
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27 views

What kind of analysis gives you the statement "If you DONT reach X amount by time T, then your chances go down by P percentage?

I am trying to model growth for data I have regarding downloads of applications. I would like to make a statement, if you "DONT reach X amount of downloads by time T, then your chances of reaching 15 ...
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27 views

A question on no. of training examples and decision trees

I have a set of around 200,000 training instances. Each training instance consists of an attribute called $duration$, a discrete integer type and a time series of floating-point values, in form of a ...
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40 views

Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias

Assumptions / Context: Let's assume that I have data that can be modeled as a dynamic linear model. To estimate the parameters (e.g., covariance matrix of the state/system equation), I use a Gibbs ...
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39 views

Large regression models and multivariate model

Large Regression models says that a regression model is large if the signal dimension $p$ is greater than number of observations $n$. In AR(2) model $y_t = a1y_{t-1} + a2y_{t-2}$ the parameter is a ...
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10 views

Signal dimension in regression model

Estimating Unknown Sparsity in Compressed Sensing is a paper about sparse signal. I am just learning the concepts. In the first paragraph, it says that when the number of observation data samples $n$ ...
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14 views

What is the procedure to compare two different period time series

I am currently working on the task that I would like to compare two different period time series like Sales in 2012 vs Sales in 2013. Kindly suggest me any statistical procedure.
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What is sparse regression model

I am learning the concepts of Sparse regression and facing initial hurdles in terminology. sparse regression model explains the definition of what is meant by sparse. When the number of samples $n$ ...
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12 views

How to model occurrence counts in groups with latent membership?

I have a dataset that describes the daily history of occurrences of a certain phenomenon P among a certain population. (These are subdivided into certain classes or forms that the phenomenon can take: ...
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58 views

Sample Mean of AR(1) model

Consider the AR(1) model with iid innovations with finite mean and variance. Also, let $X_0 = 0$. \begin{align} X_t = \phi X_{t-1} + \epsilon_t \end{align} The goal is to derive the asymptotic ...
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What is an appropriate statistical test to identify significantly different timepoints in two timecourses?

I have two timecourses. Both are the same length. Both are univariate. Each represents the average EEG signal from a unique subgroup. The two subgroups do not have the same number of subjects. I ...
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40 views

Model for probability of song reaching top 10 ranking, over time?

I'm trying to model the probability of a song reaching Billboards top 10 over time. My data has the columns "Day since release", "If reached top 10". For example, [12,1] means the song hit top 10 on ...
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19 views

Residual autocorrelation versus lagged dependent variable

When modeling time series one has the possibility to (1) model the correlational structure of the error terms as e.g. an AR(1) process (2) include the lagged dependent variable as an explanatory ...
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9 views

analysing multiple individuals in specific time points for similarities

I am looking for a suitable analysis to examine my data for the presence of foraging individuals at different time periods, and whether the individual are in the same place over time. My dataset is ...
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11 views

How can I a “multiplier effect” in time series data?

I currently have data corresponding to how often a certain set of songs were downloaded. Each song has a release date, and then the number of downloads per day going forward to today. It would look ...
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26 views

R, arima() with include.mean=TRUE, still has no mean reported

I have a regression with ARMA errors, which I am fitting with arima(). I know that the ARMA model is being fit on these residuals from the regression. My problem is ...
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35 views

Arima with xreg, rebuilding the fitted values by hand

I'm using R to do some time series estimation. I'm trying to rebuild the fitted values from an Arima model by hand to use in an Excel spreadsheet using the estimated coefficients and the input data. ...
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43 views

Probability of Achieving a Count Level in Time Series Data

I have some time-series data that displays a count value for every day: These count values begin at 1 or -1 and will continue to count up (or down) if conditions in the time series are met. If the ...
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11 views

Guides to VARMA modelling in R

I'm looking at using a VARMA model to both determine the driver so value in some advertising campaigns and also to forecast future activity. I'm looking at the paper by Takada and Bass as a reference ...
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25 views

Naive forecasting

I used to know that Naive forecast is equal ( ft is the same like the previous year) Someone told me that there is another equation used in the sales which is equal ( current year - previous year)/ ...
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24 views

Statistical method to compare time-series with different periods

I have 2 study areas Study area 1: 12 meteorological stations-years available 1981-2000(same data and step) Study area 2: 10 meteorological stations-years available 1985-2011(same data and step) ...
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26 views

combining and contrasting time course GLMs using R

I am analyzing some time course data in which I have set up a GLM using R for each subject. Each GLM I want to run is an attempt to extract estimates of different behavioral conditions effects on the ...
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185 views

Intervention With Differencing

When conducting an intervention analysis with time series data (aka Interrupted Time series) as discussed here for example one requirement I have is to estimate the total gain (or loss) due to the ...
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33 views

Determining parameters in AR model for non-stationary time series

I am currently trying to fit an AR model to some financial data. The time series $Y_t$ in levels is clearly non-stationary; however it appears the first differences $dY_t$ are stationary (and this is ...
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120 views

Steps to perform time series analysis

I'm trying estimate an autoregressive model with an exogenous variable. It's about the impact of changes in oil prices on the economy. I'm planning on regressing gdp growth rate on its own lagged ...
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62 views

ARIMA, adjustments and intervention analysis

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 ...
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35 views

Is the Durbin-Watson test appropriate for count data

In determining if there is any serial correlation in a time series of count data, is the Durbin-Watson statistic or similar approaches appropriate? I ask this question because the dwtest implemented ...
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16 views

joint p.d.f. of stationary time series variables

if a stationary time series verifies that each variable depends only on the variable before it, and the joint p.d.f. of xi and xi-1 is f(xi-1,xi), which is the joint p.d.f. of xi,xi+1,xi+2, and of ...
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34 views

stationary time series example

Please, could anyone give an example of a stationary time series? I mean, if for instance $x_{1}$, $x_{2}$, $x_{3}$, $x_{4}$, $x_{5}$ are the 5 first random variables of the series, what would be the ...
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18 views

Statistical thresholds for large time-series

I am working with matrices, of the size N*M, where each cell corresponds to the Pearson's correlation between two time series. I want to threshold each matrix such that it would retain only ...
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19 views

Forecasting ar(p) for several counties

I have a data set of prices, these prices vary across time and across area. I have 18 areas with 32 time periods. What i want to do is forecast these prices, i have found that a AR(3) process fits ...
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55 views

Variance of $\bar x$, simulation with non-iid observations

So I know that the variance of $\bar x$ is usually computed as $\frac{\sigma_x^2}{n}$, and that this assumes the observations are independent. If instead, the observations have some positive serial ...
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Time in years(x axis) shows years in .5 interval - Time Series using R [migrated]

dsales1156ts<-ts(dsales1156,frequency=365,start=c(2011,6)) # This is my time series created with daily sales data when the first sale day is 6th January 2011. plot(dsales1156ts) The ...
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19 views

Obtaining the Psi Weights of a seasonal ARIMA in R

I am trying to quantify the effect of a future random shocks on my seasonal ARIMA model. If I have understood the theory correctly, the easiest way is to express my seasonal ARIMA model in its "random ...
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1answer
30 views

What is the best test to estimate the correlation between binomial/categorical dataset?

I'm trying to analyze if there are correlations between binomial dataset. I have binomial data (presence/absence) of two variables in different periods and I need to know what is the best way to find ...
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62 views

Standard techniques for forecasting revenue growth of a company?

I was curious what sort of time series models were the standard for doing this type of analysis. I have weekly sales data for the company - I could cook up my own time series model but would like to ...
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15 views

training period selection forecast (error analysis)

I have been lately testing the best training period length to perform a forecast. I have tested it for various days of training period length, among them 60 days and 30 days. My methodology is quite ...
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21 views

Statistical Methods for Calculating Vending Machine Refill

Am looking into statics to help support a project I am undertaking. The project scope concerns intelligent replenishment / refill of vending machines. During an onsite service, a technician must ...
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34 views

How to normalize time series?

This is a general question on normalization of data so that all the variables are within the same range. Why do we normalize data in pattern classification? How to normalize time series which is ...
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62 views

Is PCA appropriate for comparing subsets of panel data?

I have a large panel (5000+ subjects, 4 variables over 182 periods), and I've identified particular Granger-causal relationship in a large subset of those subjects (30% or so). I would like to somehow ...
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28 views

R function which uses innovations algorithm?

I can't seem to find much info on the following: I have a dataset D at time t which I use to fit an ARIMA model. I forecast the value of the time series at time t + 1. Now, when I'm in t + 1, I would ...
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20 views

Determine the threshold value and number of regimes with delayed variable

I am currently working on a threshold model for the exchange rate between UK and US. I have not got background knowledge on this model so I am really stuck on how to determine the Threshold value, ...
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44 views

Using AIC to determine best ARIMA Model

I'm trying to fit an ARIMA model to housing data set. Playing around with the p's and q I was able to get an ARIMA Model (2,1,2,)(2,0,0) with an AIC value of AIC=4946.76 I used auto.arima to see if I ...
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8 views

Approaches to Subsampling to exceed a Population Mean

I've studied a large amount of Probability & Statistics, but I'm embarrassed to say I've forgotten too much of it. Would appreciate any pointers anyone has about this: I have a set of about 360 ...
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88 views

Intervention Analysis - Pulse over several periods

I have a couple weekly time series and an intervention occurred over several weeks and then for some, after a period of no intervention, began again. So, the pattern is off for a period of weeks, then ...
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66 views

Getting Residuals to be White Noise

I'm on a time series project for an undergraduate course. For the project I'm trying to come up with an ARIMA model for the housing starts data set. ...
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26 views

How do I estimate a time series regression using GMM in the way proposed by Acosta-Ormaechea and Morozumi (2013)?

In their paper Acosta-Ormaechea and Morozumi (2013) propose a use of GMM for estimating a regression in which they try to find the impact of reallocating public expenditure from some unproductive to ...
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40 views

Dynamic Time Warping for irregular time series

I have been reading a lot about Dynamic Time Warping (DTW) lately. I am very surprised that there is no literature at all on the application of DTW to irregular time series, or at least I could not ...