How to add Trend Lines in R Using Plotly — R-bloggers

(This article was first published on R – Displayr, and kindly contributed to R-bloggers) 1. Global trend lines One of the simplest methods to identify trends is to fit a ordinary least squares regression model to the data. The model most people are familiar with is the linear model, but you can add other polynomial…

via How to add Trend Lines in R Using Plotly — R-bloggers

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Machine learning at central banks

https://www.bankofengland.co.uk/working-paper/2017/machine-learning-at-central-banks

Published on 01 September 2017

Regression Analysis Essentials For Machine Learning

(This article was first published on Easy Guides, and kindly contributed to R-bloggers) Regression analysis consists of a set of machine learning methods that allow us to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x). Briefly, the goal of regression model is to build a mathematical…

via Regression Analysis Essentials For Machine Learning — R-bloggers

Modelizar el consumo eléctrico

A partir de nos prévisions pour la température, on peut tenter de prévoir la consommation électrique. Rappelons que la série de consommation électrique ressemble à ca plot(electricite[passe,”Load”],type=”l”) On peut tenter un modèle assez simple, où la consommation à la date Y_t est fonction d’une tendance linéaire a+bt, de la position dans l’année (sous une forme…

via Modéliser la consommation électrique — Freakonometrics

Estadística y Machine Learning con R

 

Estadística y Machine Learning con R recopila apuntes y materiales utilizados en la docencia de cursos de formación para funcionarios sobre estadística para administraciones públicas y estadística con R, y otros materiales utilizados en la docencia de Econometría. El manual ha sido elaborado exclusivamente con R, utilizando las librerias knitr, markdown y bookdown, que permiten editar y compilar documentos en diferentes formatos.

Estadística y Machine Learning con R

 

Outliers Detection and Intervention Analysis

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) In my previous tutorial Arima Models and Intervention Analysis we took advantage of the strucchange package to identify and date time series level shifts structural changes. Based on that, we were able to define ARIMA models with improved AIC metrics.…

via Outliers Detection and Intervention Analysis — R-bloggers