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

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266 views

Updating classification probability in logistic regression through time

I am building a predictive model that forecasts a student's probability of success at the end of a term. I’m specifically interested in whether the student succeeds or fails, where success is usually ...
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206 views

$ARIMA(p,d,q)+X_t$, Simulation over Forecasting period

I have time series data and I used an $ARIMA(p,d,q)+X_t$ as the model to fit the data. The $X_t$ is an indicator random variable that is either 0 (when I don’t see a rare event) or 1 (when I see the ...
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159 views

Multivariant time series in R. How to find lagged correlation and build model for forecasting

I'm new in the page and pretty new in statistics and R. I'm working on a project for college with the objective of finding the correlation between rain and water flow level in rivers. Once the ...
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148 views

Evaluating Time Series Prediction Performance

I have a Dynamic Naive Bayes Model trained on a couple of temporal variables. The output of the model is the prediction of P(Event) @ t+1, estimated at each ...
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170 views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
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239 views

Clustering & Time Series

I have a multivariate dataset that changes over time. I have extracted (and normalised) some features and used k-means to generate clusters over the entire span of the dataset. Now I want to see ...
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404 views

Stationarity tests for time series

I am currently working on time series modeling, especially on stationarity tests. For this purpose, I am extensively using Pfaff's book "Analysis of integrated and cointegrated time series with R" and ...
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476 views

Simulating time-series given power and cross spectral densities

I am having trouble generating a set of stationary colored time-series, given the covariance matrix (their PSDs and CSDs). I know that, given two time-series $y_{I}(t)$ and $y_{J}(t)$, I can ...
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126 views

What's the probability a rabbit will return to a (certain) forest?

Let's assume we have a forest. And there is a breed of rabbits that is visiting that forest all the time. It is possible to distinguish every individual rabbit. There are devices in that forest ...
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394 views

Asynchronous (irregular) Time Series Analysis

I am trying to analyze the lead-lag between time series of two stock prices. In regular time series analysis, we can do Cross Correlaton, VECM (Granger Causality). However how does one handle the ...
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152 views

Examining correlation and long range dependence in time series data with strong diurnal effects

I have data sets of network traffic that exhibit strong diurnal effects making them non-stationary. One of the analysis that I want to run is to show correlation between days. If we chopped up the ...
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261 views

How does pooling and resampling affect variance of sample mean?

Suppose I have $N$ independent random variables $X_n$. I draw a sample of predetermined size $K_n$ from each of them. Denote the average of each sample $\bar{\hat{X}}_n$, and the total number of ...
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526 views

Measure score change over time while accounting for baseline differences

I'd like to test for and estimate group differences in NIHSS (National Institute of Health Stroke Scale) change between hospital discharge and three months after hospital discharge. Because the score ...
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555 views

Exogenous variables in VECM

I found the following posts interesting and I was wondering if any of you guys know of good academic papers that describe methods/relationships of exogenous variables in VECM models. If so could you ...
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27 views

“Average” line over irregular log series data

I have simulation data from 1000 runs, plotting some measurable (in this case convergence of the algorithm) as a function of simulation time. Each run produces a discrete set of points ...
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127 views

How to fit log-linear poisson autoregressive mixed model?

I have time-series count data $N_{i,j}$ (population sizes in site $i$ and year $j$) and I want to correlate year-to-year changes with the environmental conditions $x_{i,j}$. For this, I want to fit ...
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95 views

Probability puzzle about zombies

I am thinking about writing a simple game about zombies. I got stuck trying to calculate how many people should become zombies. Here are my conditions: We have a small rural town of 700 people. One ...
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237 views

Detecting statistically significant clustering of continuous values

I'm working with biological sequence data where each position in the sequence has an associated continuous value. I'm ignoring the sequence content so the data is very similar to a time series with ...
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160 views

How to design good plots for multiple time series?

I have huge (about 100 000) set of time series. I need to show between 5 to 10 time series, chosen semi-randomly on one chart. Chart estate is very limited - plot for each time series is only 100px x ...
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111 views

Visualizing probability of event over time, based on epoch time

I have a list of epoch/unix times at which an event happened. My hypothesis is that there are certain times during the week when this event might happen more frequently. How can I visualize/determine ...
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123 views

Relationship between LASSO T and LARS number of steps k

We can see on the figure (cf Least Angle Regression p30, Efron, Hastie, Johnstone, Tibshirani - link: Least Angle Regression) that there is a direct relationship between: LASSO T absolute norm of ...
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159 views

Estimating parameters of an unknown PID controller

Say that I have your standard PID controller at work. To keep it extremely simple imagine I have a target $x^*$ on the variable $x$. Then the controller is: $y(t) = K_p ( x^* - x_t) + K_i \int_0^t ...
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520 views

Lag length selection Granger causality test

Consider G-Causality on two stationary time series vectors (call these variables $X$ and $Y$), each with 100+ observations. It's daily financial market time series data. I have reason to believe that ...
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51 views

Estimating a time frame for interventions or judging the extent to which events become “determined” as time goes on

In certain arenas, it's valuable to be able to intervene early on to prevent problems from getting worse, because after a certain point there's not much you can do. Two examples might be public health ...
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180 views

How to analyze mood data over time?

I've collected data for individuals on a team about their daily mood. Each day, individuals rate their mood on a scale that is assigned the following values: 0 (bad), 5 (so-so), 10 (good). Using the ...
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347 views

How to model time-varying correlation

Suppose I have two time-series variables, $\{x_t\}$ and $\{y_t\}$, where $t\in[1,T]$. I would like to model the correlation $\rho(x_t,y_s)$ as some function of $t$,$s$, and the difference $t-s$. In ...
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124 views

How to denoise a “Poissonous” time series

I have $N$ time series each of which can be modeled as $$y_{kt}=Ax_{kt}+b+\varepsilon_{kt}\quad(1\le k\le N,1\le t\le T),$$ where $x_{kt}\sim\text{Pois}(\lambda\Delta t)$ and $\varepsilon_{kt}\sim ...
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95 views

Fama and French Three Factors: A time series analysis

Is there any literature on whether the three factors in the Fama–French three-factor model follow any kind of time series models, such as multivariate ARMA?
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145 views

Why do I get very different results estimating GARCH-M model in EViews and R (rugarch)?

I'm dealing with a GARCH-M model that I've estimated using R and EViews. Here are its mean and variance equations. Mean equation: $$ y_t=\mu + \rho \sigma^2_t + \varepsilon_t $$ Variance equation: ...
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17 views

can an ARMA process with complex unit roots be made stationary by differencing?

If an ARMA process (or just a AR(p) process) has real unit roots (i.e. 1 or -1), then differencing it repeatedly will make the differenced process weakly stationary. An ARMA process (or just a AR(p) ...
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62 views

Is spurious regression impossible if you include lagged error terms?

If you have a random walk A, and a random walk B, and you regress them against each other you run into spurious regression. In our textbook however is written that it is impossible to have spurious ...
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49 views

Annual time series count data where the dependent variable is a count averaging 3,000 and no zeros

I need your assistance on time series count data. I got some annual time series data I want to run, however the dependent variable is a count (number of deaths) while the independent variables are ...
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139 views

Vector autoregression with exogenous variables

Im dealing with a VAR model where I also want to include exogenous variables. Based on my sampling, the exogenous variables in $t$ are independent from my other variables in $t$, but highly dependent ...
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89 views

Predicting for month in R

I'm trying to understand some concepts related to predictive modeling. So let's say that I have the following data sample and am trying to regress sales on ...
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84 views

Averaging Correlations

I am working with patient experience data where patients answer questions regarding their stay at the hospital. Each question is then given a correlation value as it relates to the final "overall ...
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178 views

Regression model of large, correlated, heavy-tailed data

I have a large panel-like data set: about 15,000 individuals, on average 350 time points, two dozen variables (plus some more variables we left out for context-specific reasons). What I want to ...
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121 views

Dealing with missing data in an exponential smoothing model

There does not seem to be a standard way to deal with missing data in the context of the exponential smoothing family of models. In particular, the R implementation called ets in the forecast package ...
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68 views

Determining stability in a time series via probabilistic modeling

I recently started to read Probabilistic Programming and Bayesian Methods for Hackers and really got interested in the topic and PyMC. I especially like the example of the first chapter where ...
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133 views

Algorithm for real-time normalization of time-series data?

I'm working on an algorithm that takes in a vector of the most recent data point from a number of sensor streams and compares the euclidean distance to previous vectors. The problem is that the ...
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139 views

Cointegration structure

I have two time series that I am investigating, acc and amb, the time frequency is daily data. They are both non stationary, as evidenced by the follows: ...
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131 views

Method for calculating percentiles for a set of time series

I have a set of time series for a given quantity (e.g. CPU). The measurements are roughly evenly spaced, but the data points aren't synchronised between sets and some sets have missing measurements. ...
3
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133 views

Predicting the year computers will be able to do 1 exaFLOPS using historical data (time series forcasting using prediction interval)

Put simply, I need to make a prediction interval (or confidence interval?) for the x value when y reaches a certain number given historical data for x versus y. Long Version: I have data on the ...
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193 views

Standardized dependent variable within a group in panel data models?

Does standardizing of a dependent variable within the identifying group make sense? The following working paper (Deforestation slowdown in the Legal Amazon; Prices or Policies?, pdf) uses a ...
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209 views

Best practices for dealing with shifting, inconsistent seasonality

This question is related to a previous post I've looked at (Calculation of seasonality indexes for complex seasonality), but deals with more granular data (daily instead of weekly), and transforming ...
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57 views

How to form a confidence band around the trend fitted from time series data

I have a time series data set. I can decompose it and get the trend but I would like to put confidence ranges around the trend (past) not the forecast-ed component. The decompose function also ...
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136 views

What is the best test for validating trends in time-series data?

I have annual temperature data from a variety of weather stations in the Caribbean and I want to be able to show statistically that the trends for each station are significant, either positive or ...
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70 views

T-test when observations are years

I would like to know whether I can use the t-test or the non-parametric equivalent test when I have years as observations. Suppose I want to compare the profitability levels of two companies and I ...
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176 views

Unit root tests and stationarity

Two common methods of testing whether a time series is stationary are the KPSS and ADF tests. If my understanding is correct, these tests essentially work by measuring the residuals of fitting the ...
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91 views

Prediction model problem

I am trying to design a model that can estimate the number of customers I will receive in every store every month using the number of customers I received every month in every store for the last five ...
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122 views

Reference for implementing generalized likelihood ratio test to determine online whether time-series mean has shifted

What is a reference that describes the "generalized likelihood ratio" test to determine online (i.e., meaning that we add an observation, then check, then add an observation, then check) whether the ...