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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Time Series Deep Learning, Part 2: Predicting Sunspot Frequency with Keras LSTM In R — R-bloggers

(This article was first published on business-science.io – Articles, and kindly contributed to R-bloggers) One of the ways Deep Learning can be used in business… 7,248 more words

via Time Series Deep Learning, Part 2: Predicting Sunspot Frequency with Keras LSTM In R — R-bloggers

Deep Learning for Time Series Forecasting: Predicting Sunspot Frequency with Keras — R-bloggers

(This article was first published on TensorFlow for R, and kindly contributed to R-bloggers) Forecasting sunspots with deep learning In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Here’s an image from NASA showing…

via Deep Learning for Time Series Forecasting: Predicting Sunspot Frequency with Keras — 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

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

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