2291 search results for "time series"

Causality in Time Series – A look at 2 R packages (CausalImpact and Changepoint)

November 15, 2014
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My first blog post on Perceivant.comhttp://perceivant.com/causality-time-series-determining-impact-marketing-interventions/

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CausalImpact: A new open-source package for estimating causal effects in time series

September 10, 2014
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CausalImpact: A new open-source package for estimating causal effects in time series

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...

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Specifying complicated groups of time series in hts

June 14, 2014
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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...

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Identifying periods of change in time series with GAMs

May 15, 2014
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Identifying periods of change in time series with GAMs

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....

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Time Series Analysis using R – forecast package

April 17, 2014
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Time Series Analysis using R – forecast package

In today’s blog post, we shall look into time series analysis using R package – forecast. Objective of the post will be explaining the different methods available in forecast package which can be applied while dealing with time series analysis/forecasting. What is Time Series?A time series is a collection of observations of...

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Fit an Ornstein–Uhlenbeck process with discrete time series data

April 4, 2014
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As we know, a Brownian motion is usually formulated as $$dx_t = mu,dt+sigma,dW_t$$ which is the continuous case of a random walk. In some cases, it is quite convenient to use this formulation to describe the characteristic of asset prices due to its highly unpredictable behavior. However, there are financial indicators or variables that also exhibit, at least temporarily, stable...

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Fit an Ornstein–Uhlenbeck process with discrete time series data

April 4, 2014
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As we know, a Brownian motion is usually formulated as $$dx_t = mu,dt+sigma,dW_t$$ which is the continuous case of a random walk. In some cases, it is quite convenient to use this formulation to describe the characteristic of asset prices due to its highly unpredictable behavior. However, there are financial indicators or variables that also exhibit, at least temporarily, stable...

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Seasonal, or periodic, time series

March 20, 2014
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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...

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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 →

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Fitting models to short time series

March 3, 2014
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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...

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