# 2066 search results for "time series"

## Packrat presentation at useR! 2014

September 26, 2014
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There comes a time in a software toolchain’s lifecycle where the focus shifts from developer...

## Estimating Generalization Error with the PRESS statistic

September 25, 2014
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As we’ve mentioned on previous occasions, one of the defining characteristics of data science is the emphasis on the availability of “large” data sets, which we define as “enough data that statistical efficiency is not a concern” (note that a “large” data set need not be “big data,” however you choose to define it). In Related posts:

## How Many Paths are Possible in an 18 Hole Round of Match Play Golf?

September 25, 2014
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In honor of the Ryder Cup, here's a fun puzzle for the mathematically inclined golfer to consider: how many different paths are possible in an 18 hole round of match play golf? If you'd rather not wade through the math then you can skip ahead to the "practical exploration" section of this post to see some actual match play...

## DVI Performance

September 24, 2014
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This is the next post in the DVI indicator series. After the first two (here and here) analyzed in details the post-entry returns and the entry power of this indicator, it’s time to take a look at the trading performance. Using the Systematic Investor Toolbox, we get some pretty decent results: CAGR of 16.15% and

## Factors are not first-class citizens in R

September 23, 2014
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The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R Related posts:

## Hands-on dplyr tutorial for faster data manipulation in R

September 23, 2014
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I love dplyr. It's my "go-to" package in R for data exploration, data manipulation, and feature engineering. I use dplyr because it saves me time: its performance is blazing fast on data frames, but even more importantly, I can write dplyr code faster ...

## Newcastle R course, a write-up

September 22, 2014
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I recently attended a week-long R course in Newcastle, taught by Colin Gillespie. It went from “An Introduction to R” to “Advanced Graphics” via a day each on modelling, efficiency and programming. Suffice to say it was an intense 5 days! Overall it was the best R course I’ve been on so far. I’d recommend it to others,...

## Build Predictive Model on Big data: Using R and MySQL Part-3

September 21, 2014
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Welcome to last part of the series post again! In previous part I discussed about the solutions to the questions mentioned in first part. In this part, we will implement whole scenario using R and MySQL together and see how we can process bigdata(computationally ) Let us recall those questions and summarize their answers to The post Build...

## Build Predictive Model on Big data: Using R and MySQL Part-2

September 21, 2014
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Welcome to the second part of the series blog posts. In first part we tried to understand the challenges of fitting predictive model to the large dataset. In this post I will discuss about the solution approach to that challenges. Let’s start rolling. As machine learning technique requires accessing whole dataset for fitting model on The post Build...

## Build Predictive Model on Big data: Using R and MySQL Part-1

September 21, 2014
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Wellcome to the series blog posts. Since long time, I am writing post on Machine learning with R. Today I am gonna discuss on big data problem while fitting machine learning on it and its solution using MySQL and R. Before we jump directly to solution, let us discuss about big data little bit. (You The post Build...