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

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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

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

Descomposición temporal con R

 

Resumen:

Evaluación de diferentes funciones descomposición temporal de R. Se utiliza como ejemplo la serie mensual de las concentraciones atmosféricas de CO2 en partes por millón (ppm) en Mauna Loa (Hawai),desde 1959 a 1979 .

Las funciones de R analizadas son: decompose, stl, decomp y descomponer. En los análisis gráficos muestran una gran simulitud, tanto en lo relativo a la serie de tendencia T(t), como a la estacionalidad S(t). La serie irregular I(t) acepta la hipótesis de normalidad en los test estadísticos KS y CVM en todos los casos, pero con autocorrelación serial.

 

descomposición temporal con R

 

En R-Pub:

Descomposición temporal con R

Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader.

(This article was first published on R – Curtis Miller’s Personal Website, and kindly contributed to R-bloggers) Introduction Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the…

via Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader — R-bloggers

Data Science for Business – Time Series Forecasting Part 3: Forecasting with Facebook’s Prophet.

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In my last two posts (Part 1 and Part 2), I explored time series forecasting with the timekit package. In this post, I want to compare how Facebook’s prophet performs on the same dataset. Predicting future events/sales/etc. isn’t trivial for a number…

via Data Science for Business – Time Series Forecasting Part 3: Forecasting with Facebook’s Prophet — R-bloggers