Time series

Inference and autoregressive processes

September 6, 2012 | 0 Comments

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=.5 __ phi2=-.4 __ sigma=1.5 __ set.seed(1) __ n=240 __ WN=rnorm(n,sd=sigma) __ ... [Read more...]

Descriptive statistics of some Agile feature characteristics

September 2, 2012 | 0 Comments

The purpose of software engineering research is to figure out how software development works so that the software industry can improve its quality/timeliness (i.e., lower costs and improved customer satisfaction). Research is hampered by the fact that companies are not usually willing to make public good quality data ... [Read more...]

Visualizing Euro 2012: First Group Games

June 12, 2012 | 0 Comments

Now that every team has played a match it will be interesting to see how this has affected the (inverse) odds of victory. Since the plot in my last post was a bit ‘busy’, I have decided to use the facet_wrap function in gglplot2 to stratify by group. Also, ... [Read more...]

Time-Series Policy Evaluation in R

May 21, 2012 | 0 Comments

Quantifying the success of government policies is clearly important. Randomized control trials, like those conducted by drug companies, are often described as the ‘gold-standard’ for policy evaluation. Under these, a policy is implemented in/to one area/group (treatment), but not in/to another (control). The difference in outcomes between ... [Read more...]

Temperature Change in Ireland

April 7, 2012 | 0 Comments

Has Ireland gotten any warmer? Ask any punter on the street and they will happily inform you of wild swings, trends and dips. “Back when I was a child”, “when I was younger”, or “years ago” are the usual refrains. What’s the evidence? To answer this, I will use ... [Read more...]

Time Series Intervention Analysis wih R and SAS

January 21, 2012 | 0 Comments

In a previous post, I worked through the theory behind intervention analysis. In his time series course, University of Georgia political science professor Jamie Monogan demonstrates how to implement intervention analysis in R.  The following examp...
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non-stationary AR(10)

January 18, 2012 | 0 Comments

In the revision of Bayesian Core on which Jean-Michel Marin and I worked together most of last week, having missed our CIRM break last summer (!), we have now included an illustration of what happens to an AR(p) time series when the customary stationarity+causality condition on the roots of ... [Read more...]

Functional and Parallel time series cross-validation

November 21, 2011 | 0 Comments

Rob Hyndman has a great post on his blog with example on how to cross-validate a time series model.  The basic concept is simple:  You start with a minimum number of observations (k), and fit a model (e.g. an arima model) to those observation... [Read more...]

Example 9.11: Employment plot

October 25, 2011 | 0 Comments

A facebook friend posted the picture reproduced above-- it makes the case that President Obama has been a successful creator of jobs, and also paints GW Bush as a president who lost jobs. Another friend pointed out that to be fair, all of Bush's presi...
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Generating restricted permutations with permute

October 18, 2011 | 0 Comments

In a previous post I introduced the permute package and the function shuffle(). In that post I got as far as replicating R’s base function sample(). Here I’ll briefly outline how shuffle() can be used to generate restricted permutations. shuffle() … Continue reading → [Read more...]

Simple time series plot using R : Part 2

October 4, 2011 | 0 Comments

I would like to share my experience of plotting different time series in the same plot for comparison. As an assignment I had to plot the time series of Infant mortality rate(IMR) along with the SOX emission(sulphur emission) for the past 5 decades in ...
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Simple plot using R

September 21, 2011 | 0 Comments

As a task for my Financial eco assignment I had to plot a simple time series of the overnight MIBOR(Mumbai interbank offer rates) for the past one year . The job could very well have been done easily in MS-Excel but I choose to plot it in R instea...
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Smoothing temporally correlated data

July 21, 2011 | 0 Comments

Something I have been doing a lot of work with recently are time series data, to which I have been fitting additive models to describe trends and other features of the data. When modelling temporally dependent data, we often need … Continue reading → [Read more...]

Embedding a time series with time delay in R — Part II

June 14, 2011 | 0 Comments

Some months ago, I posted a function that extended the base R function embed() to allow for time delay embedding. Today, David Gonzales alerted me to an inconsistency between embed() and Embed(). The example David used was where Embed() clearly … Continue reading → [Read more...]

Global warming since 1995 ‘now significant’

June 11, 2011 | 0 Comments

Yesterday (June 11, 2011) the BBC reported on comments by Prof. Phil Jones, of the Climatic Research Unit (CRU), University of East Anglia (UEA), that the warming experienced by the planet since 1995 was statistically significant. That the trend in … Continue reading → [Read more...]

Time series

March 28, 2011 | 0 Comments

(This post got published on The Statistics Forum yesterday.) The short book review section of the International Statistical Review sent me Raquel Prado’s and Mike West’s book, Time Series (Modeling, Computation, and Inference) to review. The current post is not about this specific book, but rather on why ... [Read more...]

The housing bubble by city

March 17, 2011 | 0 Comments

The housing bubble by city. Miami sailed high and fell far. Detroit rose modestly and but dropped more than it went up. Dallas held steady. DC is enjoying a bit of renewed growth, but are in and New York yet to fall? [Read more...]


February 9, 2011 | 0 Comments

In time series work you often run into difficulties in modeling processes where the overall level of one variable (an input, for example) changes over time but the levels of another variable (an output) do not change. For instance if … Continue reading →
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