Data Science Live Book (open source)

(This article was first published on R – Data Science Heroes Blog, and kindly contributed to R-bloggers) Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than 1 year ago tl;dr: Hi there! I invite you to read the…

via Data Science Live Book (open source) ~ new big release! 200-pages — R-bloggers

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Ensemble learning for time series forecasting in R

(This article was first published on Peter Laurinec, and kindly contributed to R-bloggers) Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible overfit. In the domain of time series forecasting, we…

via Ensemble learning for time series forecasting in R — R-bloggers

Practical Machine Learning with R and Python – Part 1

(This article was first published on R – Giga thoughts …, and kindly contributed to R-bloggers) Introduction This is the 1st part of a series of posts I intend to write on some common Machine Learning Algorithms in R and Python. In this first part I cover the following Machine Learning Algorithms Univariate Regression Multivariate…

via Practical Machine Learning with R and Python – Part 1 — R-bloggers

ARIMA models and Intervention Analysis

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) In my previous tutorial Structural Changes in Global Warming I introduced the strucchange package and some basic examples to date structural breaks in time series. In the present tutorial, I am going to show how dating structural changes (if any)…

via ARIMA models and Intervention Analysis — R-bloggers

ESS Guidelines on Seasonal Adjustment. Edición 2015

Eurostat ha publicat l’edició 2015 de les recomanacions per desestacionalització: ESS guidelines on seasonal adjustment “The revised ESS Guidelines on Seasonal Adjustment present both theoretical aspects and practical implementation issues in a friendly and easy to read framework, thereby addressing both experts and non-experts in seasonal adjustment. They meet the requirement of principle 7 (Sound […]

via ESS Guidelines on Seasonal Adjustment. Edició 2015 — Bloc d’estadística oficial

Time Series Analysis in R Part 1: The Time Series Object

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) Many of the methods used in time series analysis and forecasting have been around for quite some time but have taken a back seat to machine learning techniques in recent years. Nevertheless, time series analysis and forecasting are useful tools…

via Time Series Analysis in R Part 1: The Time Series Object — R-bloggers

Time Series Analysis in R Part 2: Time Series Transformations.

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) In Part 1 of this series, we got started by looking at the ts object in R and how it represents time series data. In Part 2, I’ll discuss some of the many time series transformation functions that are available…

via Time Series Analysis in R Part 2: Time Series Transformations — R-bloggers