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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# tsEMOS: Time Series based Ensemble Model Output Statistics
<!-- badges: start -->
[](https://CRAN.R-project.org/package=tsEMOS)
[](https://github.com/jobstdavid/tsEMOS/actions)
[](https://github.com/jobstdavid/tsEMOS)
<!-- badges: end -->
An R package for time series based extensions of Ensemble Model Output Statistics (EMOS) as described in the references.
It depends on the R-packages:
- [imputeTS](https://cran.r-project.org/web/packages/imputeTS/index.html): Time series missing value imputation.
- [rugarch](https://cran.r-project.org/web/packages/rugarch/index.html): Univariate GARCH models.
## Installation
You can install the development version from GitHub with:
``` r
# install.packages("remotes")
remotes::install_github("jobstdavid/tsEMOS")
```
## Package overview
Below is an overview of all functions contained in the R-package for model estimation and prediction:
- ``semos``: smooth EMOS (SEMOS).
- ``dar_semos``: deseasonalized autoregressive smooth EMOS (DAR-SEMOS).
- ``dargarchmult_semos``: multiplicative deseasonalized autoregressive smooth EMOS with generalized autoregressive conditional heteroscedasticity (DAR-GARCH-SEMOS ($\cdot$)).
- ``dargarchadd_semos``: additive deseasonalized autoregressive smooth EMOS with generalized autoregressive conditional heteroscedasticity (DAR-GARCH-SEMOS (+)).
- ``sar_semos``: standardized autoregressive smooth EMOS (SAR-SEMOS).
## Example
### Load R-package and data
```{r example1}
# load package
library(tsEMOS)
# load data for station Hannover
data(station)
# select data for lead time 24 hours
data <- station[station$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
```{r example2}
fit <- semos(train = train,
test = test,
doy_col = 3,
obs_col = 9,
mean_col = 10,
sd_col = 11,
n_ahead = 0)
```
### DAR-SEMOS
```{r example3}
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 ($\cdot$)
```{r example4}
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 (+)
```{r example5}
fit <- dargarchadd_semos(train = train,
test = test,
doy_col = 3,
obs_col = 9,
mean_col = 10,
sd_col = 11,
n_ahead = 0)
```
### SAR-SEMOS
```{r example6}
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 [[email protected]](mailto:[email protected]) 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.