forecast: Forecasting functions for time series and linear models

By Rob J Hyndman, George Athanasopoulos, Christoph Bergmeir, G. Caceres, Leanne Chhay, M. O'Hara-Wild, Fotios Petropoulos, Slava Razbash, Earo Wang, Farah Yasmeen. 2018

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “s seems not practical for our purpose, and limiting the amount of features is desirable, in our proposed framework we follow the second approach, and use a set of selfdescribable features proposed by Hyndman et al. (2015b) to obtain a meaningful division of clusters. These suggested features are des”
FFORMA: Feature-based forecast model averaging (2020, methodology): “We considered nine methods implemented in the forecast package in R (Hyndman et al. 2018a) for the pool of methods, P in Algorithm 1: 1. naïve (naive); 2. random walk with drift (rwf with drift=TRUE); 3. seasonal naïve (snaive).”
Forecasting with temporal hierarchies (2017, methodology): “…generate base forecasts over the test samples using the automated algorithms for ExponenTial Smoothing (ETS) and AutoRegressive Integrated Moving Average (ARIMA) models as implemented in the forecast package for R (Hyndman, 2015; R Core Team, 2012) and described in Hyndman and Khandakar (2008).”
Warming advances top-down control and reduces producer biomass in a freshwater plankton community (2017, methodology): “To check for the presence of temporal autocorrelation, the residuals from the model fits were tested with the function auto. arima (package forecast from Hyndman 2015) and ARIMA transformed when necessary.”
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil (2020, methodology): “The ARIMA modeling is performed through the use of forecast package [26, 27] with use of auto.”
Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018, unknown): “Using the remaining observations as training data, base forecasts were generated from ARIMA and ETS models using the default settings as implemented in the forecast package for R (Hyndman 2017) and described in Hyndman and Khandakar (2008).”
ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages (2016, background): “Forecasting packages such as forecast (Hyndman, 2015), changepoint (Killick et al., 2016), strucchange (Zeileis et al., 2002), and dlm (Petris, 2010), are popular choices for statisticians and researchers.”
Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018, methodology): “…in the present study s/n Name R function Category Reference R package 1 naı̈ve Simple 2 Random walk rwf Simple Hyndman and Athanasopoulos (2017) Hyndman et al. (2017) 3 ARFIMA arfima ARFIMA Hyndman et al. (2017) 4 BATS bats State space De Livera et al. (2011) Hyndman et al. (2017) 5 Simple…”
Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018, methodology): “Our work builds on the bootstrapping procedure proposed by Bergmeir et al. (2016) (implemented in the function baggedETS in version 8.1 (and later) of the forecast package (Hyndman and Khandakar, 2008; Hyndman, 2017) in the programming language R (R Core Team, 2017)).”
Forecasting in social settings: The state of the art (2020, methodology): “When applying the Theta method, we use the thetaf() implementation from the forecast package for R (Hyndman et al. 2018), to ensure consistent application to all data sets.”
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016, methodology): “, 2005), exponential smoothing methods obtained competitive results, and with the forecast package (Hyndman and Khandakar, 2008; Hyndman, 2014) in the programming language R (R Core Team, 2014), a fully automatic software for fitting ETS models is available.”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “automated algorithm of Hyndman & Khandakar (2008). A stepwise selection over possible models is performed and the best ARIMA model is returned using appropriate criteria.”
Forecasting global stock market implied volatility indices (2018, methodology): “(Hyndman et al., 2013) to forecast the decomposed series 1 , t IV .”
imputeTS: Time Series Missing Value Imputation in R (2017, unknown): “Nevertheless, there are some other packages that include imputation functions in addition to their core package functionality, the most noteworthy being zoo (Zeileis and Grothendieck, 2005) and forecast (Hyndman, 2016).”