I saw the post a time series contest on Rob J Hyndman's blog. Since I am still wanting to play around with some bigger data sets, so I went to the source website https://drive.google.com/folderview?id=0BxmzB6Xm7Ga1MGxsdlMxbGllZnM&usp=shar...

Amongst today’s email was one from someone running a private competition to classify time series. Here are the essential details. The data are measurements from a medical diagnostic machine which takes 1 measurement every second, and after 32–1000 seconds, the time series must be classified into one of two classes. Some pre-classified training data is

With so many more devices and instruments connected to the "Internet of Things" these days, there's a whole lot more time series data available to analyze. But time series are typically quite noisy: how do you distinguish a short-term tick up or down from a true change in the underlying signal? To solve this problem, Twitter created the BreakoutDetection...

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...

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