tsEMOS: Time Series based Ensemble Model Output Statistics

An R package for time series based extensions of Ensemble Model Output Statistics (EMOS) as described in the references.

It depends on the R-packages:

• imputeTS: Time series missing value imputation.
• rugarch: Univariate GARCH models.

Installation

You can install the development version from GitHub with:

# install.packages("remotes")remotes::install_github("jobstdavid/tsEMOS")

Package overview

Below is an overview of all functions contained in the R-package for model estimation and prediction:

• semos: smooth EMOS (SEMOS).
• dar_semos: deseasonalized autoregressive smooth EMOS (DAR-SEMOS).
• dargarchmult_semos: multiplicative deseasonalized autoregressive smooth EMOS with generalized autoregressive conditional heteroscedasticity (DAR-GARCH-SEMOS ($\cdot$)).
• dargarchadd_semos: additive deseasonalized autoregressive smooth EMOS with generalized autoregressive conditional heteroscedasticity (DAR-GARCH-SEMOS (+)).
• sar_semos: standardized autoregressive smooth EMOS (SAR-SEMOS).

Example

Load R-package and data

# load package
library(tsEMOS)
#> Registered S3 method overwritten by 'quantmod':#> method from#> as.zoo.data.frame zoo# load data for station Hannover
data(station)
# select data for lead time 24 hoursdata<-station[station$lt==24, ]
# split data in training and test datatrain<-data[data$date<= as.Date("2019-12-31"), ]
test<-data[data$date> as.Date("2019-12-31"), ]

SEMOS

fit<- semos(train=train,
test=test,
doy_col=3,
obs_col=9,
mean_col=10,
sd_col=11,
n_ahead=0)

DAR-SEMOS

fit<- dar_semos(train=train,
test=test,
doy_col=3,
obs_col=9,
mean_col=10,
sd_col=11,
n_ahead=0)

DAR-GARCH-SEMOS ($\cdot$)

fit<- dargarchmult_semos(train=train,
test=test,
doy_col=3,
obs_col=9,
mean_col=10,
sd_col=11,
n_ahead=0)

DAR-GARCH-SEMOS (+)

fit<- dargarchadd_semos(train=train,
test=test,
doy_col=3,
obs_col=9,
mean_col=10,
sd_col=11,
n_ahead=0)

SAR-SEMOS

fit<- sar_semos(train=train,
test=test,
doy_col=3,
obs_col=9,
mean_col=10,
sd_col=11,
n_ahead=0)

Contact

Feel free to contact jobstd@uni-hildesheim.de if you have any questions or suggestions.

References

Jobst, D., Möller, A., and Groß, J. (2024). Time Series based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing. https://doi.org/10.48550/arXiv.2402.00555.