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

learn more… | top users | synonyms

6
votes
0answers
263 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 ...
6
votes
0answers
276 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 ...
6
votes
0answers
166 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 ...
6
votes
0answers
267 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 ...
5
votes
0answers
194 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 ...
5
votes
0answers
494 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 ...
5
votes
0answers
603 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 ...
5
votes
0answers
497 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 ...
5
votes
0answers
620 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 ...
5
votes
0answers
105 views

Confidence intervals for difference in time series

I have a stochastic model used to simulate time series of some process. I am interested in the effect of changing one parameter to a specific value and want to show the difference between the time ...
5
votes
0answers
622 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 ...
4
votes
0answers
68 views

Generalized Linear Models vs Timseries models for forecasting

What are the differences in using Generalized Linear Models, such as Automatic Relevance Determination (ARD) and Ridge regression, versus Time series models like Box-Jenkins (ARIMA) or Exponential ...
4
votes
0answers
29 views

How to decide the p and q for GARCH model?

My question is simple. When shall I stop when trying the value for p and q? I have got the loglikelihood from ARCH(1) to ARCH(10). It's increasing. And then I tried GARCH(1,1), GARCH(2,1) etc. The ...
4
votes
0answers
44 views

How do I solve this stochastic differential equation?

So I have a second order stationary process $Y(t), \infty < t < \infty$ which has a continuous sample function, mean $\mu_Y = 1$ and covariance function $r_Y(t) = e^{-|t|}, -\infty < t < ...
4
votes
0answers
48 views

Methods of fitting a dynamic linear model

I'm taking a time series course and am learning about exchangeable time series form of dynamic linear models (DLMs). This is given by: \begin{align*} \mathbf{y}_t' &= ...
4
votes
0answers
94 views

Interpretation of the partial autocorrelation function for a pure MA process

I have been working with some time-series theory and I noticed something that I can understand "mathematically", but not based on the intuitive explanations of what the partial auto-correlation ...
4
votes
0answers
75 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 ...
4
votes
0answers
37 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 ...
4
votes
0answers
169 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 ...
4
votes
0answers
102 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 ...
4
votes
0answers
254 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 ...
4
votes
0answers
170 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 ...
4
votes
0answers
3k views

Confusion with Augmented Dickey Fuller test

I am working on the data set electricity available in R package tsa. My aim is to find out if an ...
4
votes
0answers
66 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 ...
4
votes
0answers
2k views

Interpreting time series decomposition using TBATS from R forecast package

I would like to decompose the following time series data into seasonal, trend, and residual componenets. The data is an hourly Cooling Energy Profile from a commercial building: ...
4
votes
0answers
224 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 ...
4
votes
0answers
207 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 ...
4
votes
0answers
115 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 ...
4
votes
0answers
151 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 ...
4
votes
0answers
132 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 ...
4
votes
0answers
175 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 ...
4
votes
0answers
52 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 ...
4
votes
0answers
135 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 ...
4
votes
0answers
657 views

What is the confidence interval calculated in a spectral density periodogram in R?

This question is similar to the one posed here: Testing significance of peaks in spectral density In that post, Pantera asks how to test whether a peak in a periodogram has a spike that is ...
4
votes
0answers
186 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 ...
4
votes
0answers
413 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 ...
4
votes
0answers
126 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 ...
3
votes
0answers
31 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 ...
3
votes
0answers
25 views

What is the ‘Pile-up Problem’?

In methods of trend-cycle decomposition, what is meant by the 'pile up problem'? How can the pile up problem be detected? Can it be detected by the method of visual inspection? If so, what are the ...
3
votes
0answers
28 views

Diferrencing vs Moving Average

Moving Average and differencing a series can both be used to remove seasonality. Does the difference of these two lie in the model they are used? Moving Average used in classical decomposition and ...
3
votes
0answers
28 views

Dynamic Time Wrapping for finding divergence in timeseries data

I have the time series information of various S&P500 sectors. I need to find which sectors are outliers and diverging from the bunch of sectors. As you can see in image below, in month of October, ...
3
votes
0answers
49 views

Find the median individual with observations on multiple variables over time

I have a question regarding the use of median. We collected data concerning disease development on hosts. We gathered the evolution of three variables over time on individuals. Their characteristics: ...
3
votes
0answers
73 views

Where can I find resources to learn about change-point analysis ?

Where can I find resources to learn about change-point analysis ? Hopefully, someone can advise me a textbook to read and it will cover both univariate change-point analysis and multivariate ...
3
votes
0answers
202 views

ACF and PACF plot analysis

I am new to ARIMA, and I am trying to understand these lag plots. Are the following ACF and PACF suggesting that the lag of my time series is 4? If I am wrong, please help me understand these plots. ...
3
votes
0answers
35 views

Is ARIMA(1,0,0)+xreg for level shift the same as linear regression model with level shift adjustment and lag1 term?

I have a time series with a level shift. Thus, when treating it with an ARIMA model, I use arima(1,0,0)+xreg. The xreg is a ...
3
votes
0answers
115 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?
3
votes
0answers
188 views

Time Series: Seasonality and trend

I am interested in financial time series and I have a small question regarding the use of the forecast package. The time series I am interested in is a monthly one and present clear evidences of ...
3
votes
0answers
430 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: ...
3
votes
0answers
67 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 ...
3
votes
0answers
74 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 ...