2328 search results for "TIME SERIES"

A time series classification contest

December 14, 2014
By
A time series classification contest

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

Read more »

Twitter’s R package for detecting breakouts in time series

November 24, 2014
By
Twitter’s R package for detecting breakouts in time series

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...

Read more »

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

November 15, 2014
By

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

Read more »

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

September 10, 2014
By
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...

Read more »

Specifying complicated groups of time series in hts

June 14, 2014
By

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...

Read more »

Identifying periods of change in time series with GAMs

May 15, 2014
By
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....

Read more »

Time Series Analysis using R – forecast package

April 17, 2014
By
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...

Read more »

Fit an Ornstein–Uhlenbeck process with discrete time series data

April 4, 2014
By

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...

Read more »

Fit an Ornstein–Uhlenbeck process with discrete time series data

April 4, 2014
By

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...

Read more »

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 »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



http://www.eoda.de







ODSC

ODSC

CRC R books series





Six Sigma Online Training





Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)