My first blog post on Perceivant.comhttp://perceivant.com/causality-time-series-determining-impact-marketing-interventions/

My first blog post on Perceivant.comhttp://perceivant.com/causality-time-series-determining-impact-marketing-interventions/

How can we measure the number of additional clicks or sales that an AdWords campaign generated? How can we estimate the impact of a new feature on app downloads? How do we compare the effectiveness of publicity across countries?In principle, all of these questions can be answered through causal inference.In practice, estimating a causal effect...

With the latest version of the hts package for R, it is now possible to specify rather complicated grouping structures relatively easily. All aggregation structures can be represented as hierarchies or as cross-products of hierarchies. For example, a hierarchical time series may be based on geography: country, state, region, store. Often there is also a separate product hierarchy: product...

In previous posts (here and here) I looked at how generalized additive models (GAMs) can be used to model non-linear trends in time series data. In my previous post I extended the modelling approach to deal with seasonal data where we model both the within year (seasonal) and between year (trend) variation with separate smooth functions....

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...

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...

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...

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

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 10 period <- 10 fc <- 1/period fs <- 1 n...