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

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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

Using regression trees for forecasting double-seasonal time series with trend in R

(This article was first published on Peter Laurinec, and kindly contributed to R-bloggers) After blogging break caused by writing research papers, I managed to secure time to write something new about time series forecasting. This time I want to share with you my experiences with seasonal-trend time series forecasting using simple regression trees. Classification and…

via Using regression trees for forecasting double-seasonal time series with trend in R — R-bloggers

Estadística y Machine Learning con R

Introducción

Capítulo I: Modelo lineal general.

Capítulo II: Extensiones al modelo de regresión lineal

Capítulo III: Modelos con Variables Cualitativas

Capítulo IV: Métodos de clasificación

Capítulo V: Agrupación de la información

Capítulo VI: Inferencia no parámetrica

Bilbliografía