1538 search results for "Time series"

Seasonal, or periodic, time series

March 20, 2014
By
Seasonal, or periodic, time series

Monday, in our MAT8181 class, we’ve discussed seasonal unit roots from a practical perspective (the theory will be briefly mentioned in a few weeks, once we’ve seen multivariate models). Consider some time series , for instance traffic on French roads, > autoroute=read.table( + "http://freakonometrics.blog.free.fr/public/data/autoroute.csv", + header=TRUE,sep=";") > X=autoroute$a100 > T=1:length(X) > plot(T,X,type="l",xlim=c(0,120)) > reg=lm(X~T) > abline(reg,col="red") As discussed in a...

Read more »

Displaying time series, spatial, and space-time data with R is available for pre-order

Displaying time series, spatial, and space-time data with R is available for pre-order

Two years ago, motivated by a proposal from John Kimmel, Executive Editor at Chapman & Hall/CRC Press, I started working …Sigue leyendo →

Read more »

Fitting models to short time series

March 3, 2014
By
Fitting models to short time series

Following my post on fitting models to long time series, I thought I’d tackle the opposite problem, which is more common in business environments. I often get asked how few data points can be used to fit a time series model. As with almost all sample size questions, there is no easy answer. It depends on the number of model parameters...

Read more »

More time series data online

February 27, 2014
By

Earlier this week I had coffee with Ben Fulcher who told me about his online collection comprising about 30,000 time series, mostly medical series such as ECG measurements, meteorological series, birdsong, etc. There are some finance series, but not ma...

Read more »

Nonlinear Time Series just appeared

February 25, 2014
By
Nonlinear Time Series just appeared

My friends Randal Douc and Éric Moulines just published this new time series book with David Stoffer. (David also wrote Time Series Analysis and its Applications with Robert Shumway a year ago.) The books reflects well on the research of Randal and Éric over the past decade, namely convergence results on Markov chains for validating

Read more »

demodulating time series

February 17, 2014
By
demodulating time series

This posting shows how one might perform demodulation in R. It is assumed that readers are generally familiar tith the procedure.First, create some fake data, a carrier signal with period 10, modulated over a long timescale, and with phase drifting linearly over time.1 2 3 4 5 6 7 8 9 10period <- 10 fc <- 1/period fs <- 1 n...

Read more »

Automatic time series forecasting in Granada

January 30, 2014
By

In two weeks I am presenting a workshop at the University of Granada (Spain) on Automatic Time Series Forecasting. Unlike most of my talks, this is not intended to be primarily about my own research. Rather it is to provide a state-of-the-art overview of the topic (at a level suitable for Masters students in Computer Science). I thought I’d provide...

Read more »

Inference for ARMA(p,q) Time Series

January 30, 2014
By
Inference for ARMA(p,q) Time Series

As we mentioned in our previous post, as soon as we have a moving average part, inference becomes more complicated. Again, to illustrate, we do not need a two general model. Consider, here, some  process, where  is some white noise, and assume further that . > theta=.7 > phi=.5 > n=1000 > Z=rep(0,n) > set.seed(1) > e=rnorm(n) > for(t...

Read more »

Inference for MA(q) Time Series

January 29, 2014
By
Inference for MA(q) Time Series

Yesterday, we’ve seen how inference for time series was possible.  I started  with that one because it is actually the simple case. For instance, we can use ordinary least squares. There might be some possible bias (see e.g. White (1961)), but asymptotically, estimators are fine (consistent, with asymptotic normality). But when the noise is (auto)correlated, then it is more...

Read more »

Inference for AR(p) Time Series

January 28, 2014
By
Inference for AR(p) Time Series

Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, > phi1=.25 > phi2=.7 > n=1000 > set.seed(1) > e=rnorm(n) > Z=rep(0,n) > for(t in 3:n) Z=phi1*Z+phi2*Z+e > Z=Z > n=length(Z) > plot(Z,type="l") Here, we have to estimate two sets of parameters: the autoregressive...

Read more »