1910 search results for "time series"

Ensemble Methods, part 1

November 17, 2013
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Ensemble Methods, part 1

Last week I dabbled in building classification trees with the party and rpart packages.  Now, I want to put together a series where I can apply those basic trees along with advanced techniques like bagging, boosting and random forest.  Additi...

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Linear Regression with R : step by step implementation part-1

November 16, 2013
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Linear Regression with R : step by step implementation part-1

Welcome to the first part of my series blog post. In this post, I will discuss about how to implement linear regression step by step in R by understanding the concept of regression. I will try to explain the concept of linear regression in very short manner and try to convert mathematical formulas in to codes(hope you The post Linear...

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Evaluating Quandl Data Quality

November 15, 2013
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Quandl has indexed millions of time-series datasets from over 400 sources. All of Quandl’s datasets are open and free. This is great news but before performing any backtest using Quandl data, I want to compare it with a trusted source: Bloomberg for the purpose of this post. I will focus only on daily Futures data here

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Daily Tallies on R

November 14, 2013
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Daily Tallies on R

Say you have a dataset, where each row has a date or time, and something is recorded for that date and time. If each row is a unique date - great! If not, you may have rows with the same date, and you have to combine records for the same date to get a daily tally.

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Iterators in R

November 13, 2013
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According to Wikipedia, an iterator is “an object that enables a programmer to traverse a container”. A collection of items (stashed in a container) can be thought of as being “iterable” if there is a logical progression from one element to the next (so a list is iterable, while a set is not). An iterator

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A statistical review of ‘Thinking, Fast and Slow’ by Daniel Kahneman

November 11, 2013
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A statistical review of ‘Thinking, Fast and Slow’ by Daniel Kahneman

I failed to find Kahneman’s book in the economics section of the bookshop, so I had to ask where it was.  ”Oh, that’s in the psychology section.”  It should have also been in the statistics section. He states that his collaboration with Amos Tversky started with the question: Are humans good intuitive statisticians? The wrong The post A...

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The R Backpages

November 7, 2013
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The R Backpages

by Joseph Rickert As an avid newspaper reader (I still get the print edition of the New York Times delivered every Sunday morning) I have always thought that some of the most interesting news is to be found in the back pages. So, in that spirit here are some things that I thought might be fit to print. Plotly...

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quantstrat is slow

November 4, 2013
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The complaint I hear most frequently about quantstrat is that it's slow, especially for large data.  Some of this slow performance is due to quantstrat treating all strategies as path-dependent by default.  Path dependence requires rules to b...

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Some Weekend Reading

November 1, 2013
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Just what you need - some more interesting reading! Al-Sadoon, M. M., 2013. Geometric and long run aspects of Granger causality. Mimeo., Universitat Pompeu Fabra. (Forthcoming in Journal of Econometrics.) Barnett, W. A. and I. Kalondo-Kanyama, 2013. Time-varying parameter in the almost ideal demand system and the Rotterdam model: Will the best specification please stand up? Working Paper 335, Econometric...

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Data Preparation – Part I

October 31, 2013
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Data Preparation – Part I

The R language provides tools for modeling and visualization, but is still an excellent tool for handling/preparing data. As C++ or python, there is some tricks that bring performance, make the code clean or both, but especially with R these choices can have a huge impact on performance and the “size” of your code. A The post Data...

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