Forecasts: Prophet Vs ETS forecast: Hyndman · Issue

Forecasting: Principles and Practice – OTexts

Notice that the ARIMA model fits the training data slightly better than the ETS model, but that the ETS model provides more accurate forecasts on the test set. A good fit to training data is never an indication that the model will forecast well. Below we generate and plot forecasts from an ETS model for the next 3 …

Why are forecasts from ARIMA and ETS equivalents different?

ETS models have ARIMA equivalents – this is described, eg, here and here. However when fitting pairs of ARIMA and ETS equivalents in R I sometimes get different results. For example, compare forecasts from ARIMA(0,2,2) and ETS(AAN):


Package ‘forecast’ – The Comprehensive R Archive Network

forecast-package Forecasting Functions for Time Series and Linear Models Description Methods and tools for displaying and analysing univariate time series forecasts including exponen-

Forecasting: Principles and Practice – OTexts

Instead, all forecasting in this book concerns prediction of data at future times using observations collected in the past. We have also simplified the chapter on exponential smoothing, and added new chapters on dynamic regression forecasting, hierarchical forecasting and practical forecasting issues.

New in forecast 4.0 | Rob J Hyndman

A few days ago I released version 4.0 of the forecast package for R.There were quite a few changes and new features, so I thought it deserved a new version number. I keep a list of changes in the Changelog for the package, but I doubt that many people look at it. So for the record, here are the most important changes to the forecast package made since v3.0 was released.

Better prediction intervals for time series forecasts | R

Hyndman avoids referring the issue in the first post linked to above by making claims only about point estimates «If you only want point forecasts, that (average of ets and auto.arima) is the best approach available in the forecast package.»

Hyndsight | Rob J Hyndman

When the data are time series, it is useful to compute one-step forecasts on the test data. For some reason, this is much more commonly done by people trained in machine learning rather than statistics. If you are using the forecast package in R, it is easily done with ETS and ARIMA models. Read More…

time series – Comparison between ARIMA and ETS models

The ETS model (ets()) was chosen based on minimising the model accuracy errors and the rolling forecast accuracy errors (in the latter, I chose the best 3 from the former, and the ets() model was competing against hw() and hw() with multiplicative seasonality, …

Please select a longer horizon when the forecasts are

You are mixing functions that create forecasts directly (like meanf()) with functions that generate models (like ets()). For functions that generate forecasts directly, you need to specify the forecast horizon when you call the function.


Automatic Time Series Forecasting: The forecast Package for R

The forecast package implements automatic forecasting using exponential smoothing, ARIMA models, the Theta method (Assimakopoulos and Nikolopoulos2000), cubic splines (Hyndman et al. 2005a), as well as other common forecasting methods.

Published in: Journal of Statistical Software · 2008Authors: Robin John Hyndman · Yeasmin KhandakarAbout: Exponential smoothing · Autoregressive integrated moving average · State-space repr…

Forecasting PM2.5 with forecast and prophet ·

This allows the time series models to be a little bit more robust in comparison to other models. Once again, I’m also using the prophet() forecast function to forecast my regressors that I’m passing into the final prophet model to predict PM2.5.