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 hours
data <- station[station$lt == 24, ]

# split data in training and test data
train <- 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.