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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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

Handbook on Cyclical Composite indicators – 2017 edition

Eurostat i l’United Nations Statistics Division, publiquen conjuntament el manual, “Handbook on Cyclical Composite indicators – 2017 edition. For Business cycle analysis” a fi que serveixi de guia per la compilació d’indicadors compostos cíclics. “The 2007-2009 global financial and economic crises revealed severe weaknesses in the system of macroeconomic infra-annual statistics which prevent a prompt […]

via “Handbook on Cyclical Composite indicators – 2017 edition” — Bloc d’estadística oficial

Tidy Time Series Analysis, Part 4: Lags and Autocorrelation

(This article was first published on business-science.io – Articles, and kindly contributed to R-bloggers) In the fourth part in a series on Tidy Time Series Analysis, we’ll investigate lags and autocorrelation, which are useful in understanding seasonality and form the basis for autoregressive forecast models such as AR, ARMA, ARIMA, SARIMA (basically any forecast model…

via Tidy Time Series Analysis, Part 4: Lags and Autocorrelation — R-bloggers