Time series cross-validation is now available in crossval, using function `crossval::crossval_ts`

. Main parameters for `crossval::crossval_ts`

include:

`fixed_window`

described below in sections 1 and 2, and indicating if the training set’s size is fixed or increasing through cross-validation iterations`initial_window`

: the number of points in the rolling training set`horizon`

: the number of points in the rolling testing set

Yes, this type of functionality exists in packages such as `caret`

, or `forecast`

, but with different flavours. We start by installing crossval from its online repository (in R’s console):

```
library(devtools)
devtools::install_github("thierrymoudiki/crossval")
library(crossval)
```

## 1 - Calling `crossval_ts`

with option `fixed_window = TRUE`

`initial_window`

is the length of the training set, depicted in blue, which is **fixed** through cross-validation iterations. `horizon`

is the length of the testing set, in orange.

### 1 - 1 Using statistical learning functions

```
data("AirPassengers")
# regressors including trend
xreg <- cbind(1, 1:length(AirPassengers))
# cross validation with least squares regression
res <- crossval_ts(y=AirPassengers, x=xreg, fit_func = crossval::fit_lm,
predict_func = crossval::predict_lm,
initial_window = 10,
horizon = 3,
fixed_window = TRUE)
# print results
print(colMeans(res))
```

```
ME RMSE MAE MPE MAPE
0.16473829 71.42382836 67.01472299 0.02345201 0.22106607
```

### 1 - 2 Using time series functions from package `forecast`

```
res <- crossval_ts(y=AirPassengers, initial_window = 10,
horizon = 3,
fcast_func = forecast::thetaf,
fixed_window = TRUE)
print(colMeans(res))
```

```
ME RMSE MAE MPE MAPE
2.657082195 51.427170382 46.511874693 0.003423843 0.155428590
```

## 2 - Calling `crossval_ts`

with option `fixed_window = FALSE`

`initial_window`

is the length of the training set, in blue, which **increases** through cross-validation iterations. `horizon`

is the length of the testing set, depicted in orange.

### 2 - 1 Using statistical learning functions

```
# regressors including trend
xreg <- cbind(1, 1:length(AirPassengers))
# cross validation with least squares regression
res <- crossval_ts(y=AirPassengers, x=xreg, fit_func = crossval::fit_lm,
predict_func = crossval::predict_lm,
initial_window = 10,
horizon = 3,
fixed_window = FALSE)
# print results
print(colMeans(res))
```

```
ME RMSE MAE MPE MAPE
11.35159629 40.54895772 36.07794747 -0.01723816 0.11825111
```

### 2 - 2 Using time series functions from package `forecast`

```
res <- crossval_ts(y=AirPassengers, initial_window = 10,
horizon = 3,
fcast_func = forecast::thetaf,
fixed_window = FALSE)
print(colMeans(res))
```

```
ME RMSE MAE MPE MAPE
2.670281455 44.758106487 40.284267136 0.002183707 0.135572333
```

**Note:** I am currently looking for a *gig*. You can hire me on Malt or send me an email: **thierry dot moudiki at pm dot me**. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!

Under License Creative Commons Attribution 4.0 International.