A tutorial on tidy cross-validation with R
Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Building a shiny app to explore historical newspapers: a step-by-step guide
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
Curly-Curly, the successor of Bang-Bang
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Fast food, causality and R packages, part 1
Fast food, causality and R packages, part 2
For posterity: install {xml2} on GNU/Linux distros
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick}
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
Historical newspaper scraping with {tesseract} and R
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Intermittent demand, Croston and Die Hard
Looking into 19th century ads from a Luxembourguish newspaper with R
Making sense of the METS and ALTO XML standards
Manipulate dates easily with {lubridate}
Manipulating strings with the {stringr} package
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
Modern R with the tidyverse is available on Leanpub
Objects types and some useful R functions for beginners
Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
R or Python? Why not both? Using Anaconda Python within R with {reticulate}
Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
Some fun with {gganimate}
Statistical matching, or when one single data source is not enough
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The never-ending editor war (?)
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
Using cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius
Using linear models with binary dependent variables, a simulation study
Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
What hyper-parameters are, and what to do with them; an illustration with ridge regression
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

In this blog post, I’ll use the data that I cleaned in a previous
blog post, which you can download
here. If you want to follow along,
download the monthly data. In my last blog post
I showed how to perform a grid search the “tidy” way. As an example, I looked for the right
hyperparameters of a SARIMA model. However, the goal of the post was not hyperparameter optimization
per se, so I did not bother with tuning the hyperparameters on a validation set, and used the test
set for both validation of the hyperparameters and testing the forecast. Of course, this is not great
because doing this might lead to overfitting the hyperparameters to the test set. So in this blog post
I split my data into trainig, validation and testing sets and use a genetic algorithm to look
for the hyperparameters. Again, this is not the most optimal way to go about this problem, since
the `{forecast}`

package contains the very useful `auto.arima()`

function. I just wanted to see
what kind of solution a genetic algorithm would return, and also try different cost functions.
If you’re interested, read on!

Let’s first load some libraries and define some helper functions (the helper functions were explained in the previous blog posts):

```
library(tidyverse)
library(forecast)
library(rgenoud)
library(parallel)
library(lubridate)
library(furrr)
library(tsibble)
library(brotools)
ihs <- function(x){
log(x + sqrt(x**2 + 1))
}
to_tibble <- function(forecast_object){
point_estimate <- forecast_object$mean %>%
as_tsibble() %>%
rename(point_estimate = value,
date = index)
upper <- forecast_object$upper %>%
as_tsibble() %>%
spread(key, value) %>%
rename(date = index,
upper80 = `80%`,
upper95 = `95%`)
lower <- forecast_object$lower %>%
as_tsibble() %>%
spread(key, value) %>%
rename(date = index,
lower80 = `80%`,
lower95 = `95%`)
reduce(list(point_estimate, upper, lower), full_join)
}
```

Now, let’s load the data:

`avia_clean_monthly <- read_csv("https://raw.githubusercontent.com/b-rodrigues/avia_par_lu/master/avia_clean_monthy.csv")`

```
## Parsed with column specification:
## cols(
## destination = col_character(),
## date = col_date(format = ""),
## passengers = col_double()
## )
```

Let’s split the data into a train set, a validation set and a test set:

```
avia_clean_train <- avia_clean_monthly %>%
select(date, passengers) %>%
filter(year(date) < 2013) %>%
group_by(date) %>%
summarise(total_passengers = sum(passengers)) %>%
pull(total_passengers) %>%
ts(., frequency = 12, start = c(2005, 1))
avia_clean_validation <- avia_clean_monthly %>%
select(date, passengers) %>%
filter(between(year(date), 2013, 2016)) %>%
group_by(date) %>%
summarise(total_passengers = sum(passengers)) %>%
pull(total_passengers) %>%
ts(., frequency = 12, start = c(2013, 1))
avia_clean_test <- avia_clean_monthly %>%
select(date, passengers) %>%
filter(year(date) >= 2016) %>%
group_by(date) %>%
summarise(total_passengers = sum(passengers)) %>%
pull(total_passengers) %>%
ts(., frequency = 12, start = c(2016, 1))
logged_test_data <- ihs(avia_clean_test)
logged_validation_data <- ihs(avia_clean_validation)
logged_train_data <- ihs(avia_clean_train)
```

I will train the models on data from 2005 to 2012, look for the hyperparameters on data from 2013
to 2016 and test the accuracy on data from 2016 to March 2018. For this kind of exercise, the ideal
situation would be to perform cross-validation. Doing this with time-series data is not obvious
because of the autocorrelation between observations, which would be broken by sampling independently
which is required by CV. Also, if for example you do leave-one-out CV,
you would end up trying to predict a point in, say, 2017, with data
from 2018, which does not make sense. So you should be careful about that. `{forecast}`

is able
to perform CV for time series and `scikit-learn`

, the
Python package, is able to perform
cross-validation of time series data
too. I will not do it in this blog post and simply focus on the genetic algorithm part.

Let’s start by defining the cost function to minimize. I’ll try several, in the first one I will minimize the RMSE:

```
cost_function_rmse <- function(param, train_data, validation_data, forecast_periods){
order <- param[1:3]
season <- c(param[4:6], 12)
model <- purrr::possibly(arima, otherwise = NULL)(x = train_data, order = order,
seasonal = season,
method = "ML")
if(is.null(model)){
return(9999999)
} else {
forecast_model <- forecast::forecast(model, h = forecast_periods)
point_forecast <- forecast_model$mean
sqrt(mean(point_forecast - validation_data) ** 2)
}
}
```

If `arima()`

is not able to estimate a model for the given parameters, I force it to return `NULL`

,
and in that case force the cost function to return a very high cost. If a model was successfully estimated,
then I compute the RMSE.

Let’s also take a look at what `auto.arima()`

says:

```
starting_model <- auto.arima(logged_train_data)
summary(starting_model)
```

```
## Series: logged_train_data
## ARIMA(3,0,0)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ar2 ar3 sma1 drift
## 0.2318 0.2292 0.3661 -0.8498 0.0029
## s.e. 0.1016 0.1026 0.1031 0.2101 0.0010
##
## sigma^2 estimated as 0.004009: log likelihood=107.98
## AIC=-203.97 AICc=-202.88 BIC=-189.38
##
## Training set error measures:
## ME RMSE MAE MPE MAPE
## Training set 0.0009924108 0.05743719 0.03577996 0.006323241 0.3080978
## MASE ACF1
## Training set 0.4078581 -0.02707016
```

Let’s compute the cost at this vector of parameters:

```
cost_function_rmse(c(1, 0, 2, 2, 1, 0),
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = 65)
```

`## [1] 0.1731473`

Ok, now let’s start with optimizing the hyperparameters. Let’s help the genetic algorithm a little bit by defining where it should perform the search:

`domains <- matrix(c(0, 3, 0, 2, 0, 3, 0, 3, 0, 2, 0, 3), byrow = TRUE, ncol = 2)`

This matrix constraints the first parameter to lie between 0 and 3, the second one between 0 and 2, and so on.

Let’s call the `genoud()`

function from the `{rgenoud}`

package, and use 8 cores:

```
cl <- makePSOCKcluster(8)
clusterExport(cl, c('logged_train_data', 'logged_validation_data'))
tic <- Sys.time()
auto_arima_rmse <- genoud(cost_function_rmse,
nvars = 6,
data.type.int = TRUE,
starting.values = c(1, 0, 2, 2, 1, 0), # <- from auto.arima
Domains = domains,
cluster = cl,
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = length(logged_validation_data),
hard.generation.limit = TRUE)
toc_rmse <- Sys.time() - tic
```

`makePSOCKcluster()`

is a function from the `{parallel}`

package. I must also *export* the global
variables `logged_train_data`

or `logged_validation_data`

. If I don’t do that, the workers called
by `genoud()`

will not *know* about these variables and an error will be returned. The option
`data.type.int = TRUE`

force the algorithm to look only for integers, and `hard.generation.limit = TRUE`

forces the algorithm to stop after 100 generations.

The process took 7 minutes, which is faster than doing the grid search. What was the solution found?

`auto_arima_rmse`

```
## $value
## [1] 0.0001863039
##
## $par
## [1] 3 2 1 1 2 1
##
## $gradients
## [1] NA NA NA NA NA NA
##
## $generations
## [1] 11
##
## $peakgeneration
## [1] 1
##
## $popsize
## [1] 1000
##
## $operators
## [1] 122 125 125 125 125 126 125 126 0
```

Let’s train the model using the `arima()`

function at these parameters:

```
best_model_rmse <- arima(logged_train_data, order = auto_arima_rmse$par[1:3],
season = list(order = auto_arima_rmse$par[4:6], period = 12),
method = "ML")
summary(best_model_rmse)
```

```
##
## Call:
## arima(x = logged_train_data, order = auto_arima_rmse$par[1:3], seasonal = list(order = auto_arima_rmse$par[4:6],
## period = 12), method = "ML")
##
## Coefficients:
## ar1 ar2 ar3 ma1 sar1 sma1
## -0.6999 -0.4541 -0.0476 -0.9454 -0.4996 -0.9846
## s.e. 0.1421 0.1612 0.1405 0.1554 0.1140 0.2193
##
## sigma^2 estimated as 0.006247: log likelihood = 57.34, aic = -100.67
##
## Training set error measures:
## ME RMSE MAE MPE MAPE
## Training set -0.0006142355 0.06759545 0.04198561 -0.005408262 0.3600483
## MASE ACF1
## Training set 0.4386693 -0.008298546
```

Let’s extract the forecasts:

```
best_model_rmse_forecast <- forecast::forecast(best_model_rmse, h = 65)
best_model_rmse_forecast <- to_tibble(best_model_rmse_forecast)
```

```
## Joining, by = "date"
## Joining, by = "date"
```

```
starting_model_forecast <- forecast(starting_model, h = 65)
starting_model_forecast <- to_tibble(starting_model_forecast)
```

```
## Joining, by = "date"
## Joining, by = "date"
```

and plot the forecast to see how it looks:

```
avia_clean_monthly %>%
group_by(date) %>%
summarise(total = sum(passengers)) %>%
mutate(total_ihs = ihs(total)) %>%
ggplot() +
ggtitle("Minimization of RMSE") +
geom_line(aes(y = total_ihs, x = date), colour = "#82518c") +
scale_x_date(date_breaks = "1 year", date_labels = "%m-%Y") +
geom_ribbon(data = best_model_rmse_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#666018", alpha = 0.2) +
geom_line(data = best_model_rmse_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#8e9d98") +
geom_ribbon(data = starting_model_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#98431e", alpha = 0.2) +
geom_line(data = starting_model_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#a53031") +
theme_blog()
```

The yellowish line and confidence intervals come from minimizing the genetic algorithm, and the
redish from `auto.arima()`

. Interesting; the point estimate is very precise, but the confidence
intervals are very wide. Low bias, high variance.

Now, let’s try with another cost function, where I minimize the BIC, similar to the `auto.arima()`

function:

```
cost_function_bic <- function(param, train_data, validation_data, forecast_periods){
order <- param[1:3]
season <- c(param[4:6], 12)
model <- purrr::possibly(arima, otherwise = NULL)(x = train_data, order = order,
seasonal = season,
method = "ML")
if(is.null(model)){
return(9999999)
} else {
BIC(model)
}
}
```

Let’s take a look at the cost at the parameter values returned by `auto.arima()`

:

```
cost_function_bic(c(1, 0, 2, 2, 1, 0),
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = 65)
```

`## [1] -184.6397`

Let the genetic algorithm run again:

```
cl <- makePSOCKcluster(8)
clusterExport(cl, c('logged_train_data', 'logged_validation_data'))
tic <- Sys.time()
auto_arima_bic <- genoud(cost_function_bic,
nvars = 6,
data.type.int = TRUE,
starting.values = c(1, 0, 2, 2, 1, 0), # <- from auto.arima
Domains = domains,
cluster = cl,
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = length(logged_validation_data),
hard.generation.limit = TRUE)
toc_bic <- Sys.time() - tic
```

This time, it took 6 minutes, a bit slower than before. Let’s take a look at the solution:

`auto_arima_bic`

```
## $value
## [1] -201.0656
##
## $par
## [1] 0 1 1 1 0 1
##
## $gradients
## [1] NA NA NA NA NA NA
##
## $generations
## [1] 12
##
## $peakgeneration
## [1] 1
##
## $popsize
## [1] 1000
##
## $operators
## [1] 122 125 125 125 125 126 125 126 0
```

Let’s train the model at these parameters:

```
best_model_bic <- arima(logged_train_data, order = auto_arima_bic$par[1:3],
season = list(order = auto_arima_bic$par[4:6], period = 12),
method = "ML")
summary(best_model_bic)
```

```
##
## Call:
## arima(x = logged_train_data, order = auto_arima_bic$par[1:3], seasonal = list(order = auto_arima_bic$par[4:6],
## period = 12), method = "ML")
##
## Coefficients:
## ma1 sar1 sma1
## -0.6225 0.9968 -0.832
## s.e. 0.0835 0.0075 0.187
##
## sigma^2 estimated as 0.004145: log likelihood = 109.64, aic = -211.28
##
## Training set error measures:
## ME RMSE MAE MPE MAPE
## Training set 0.003710982 0.06405303 0.04358164 0.02873561 0.3753513
## MASE ACF1
## Training set 0.4553447 -0.03450603
```

And let’s plot the results:

```
best_model_bic_forecast <- forecast::forecast(best_model_bic, h = 65)
best_model_bic_forecast <- to_tibble(best_model_bic_forecast)
```

```
## Joining, by = "date"
## Joining, by = "date"
```

```
avia_clean_monthly %>%
group_by(date) %>%
summarise(total = sum(passengers)) %>%
mutate(total_ihs = ihs(total)) %>%
ggplot() +
ggtitle("Minimization of BIC") +
geom_line(aes(y = total_ihs, x = date), colour = "#82518c") +
scale_x_date(date_breaks = "1 year", date_labels = "%m-%Y") +
geom_ribbon(data = best_model_bic_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#5160a0", alpha = 0.2) +
geom_line(data = best_model_bic_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#208480") +
geom_ribbon(data = starting_model_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#98431e", alpha = 0.2) +
geom_line(data = starting_model_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#a53031") +
theme_blog()
```

The solutions are very close, both in terms of point estimates and confidence intervals. Bias increased, but variance lowered… This gives me an idea! What if I minimize the RMSE, while keeping the number of parameters low, as a kind of regularization? This is somewhat what minimising BIC does, but let’s try to do it a more “naive” approach:

```
cost_function_rmse_low_k <- function(param, train_data, validation_data, forecast_periods, max.order){
order <- param[1:3]
season <- c(param[4:6], 12)
if(param[1] + param[3] + param[4] + param[6] > max.order){
return(9999999)
} else {
model <- purrr::possibly(arima, otherwise = NULL)(x = train_data,
order = order,
seasonal = season,
method = "ML")
}
if(is.null(model)){
return(9999999)
} else {
forecast_model <- forecast::forecast(model, h = forecast_periods)
point_forecast <- forecast_model$mean
sqrt(mean(point_forecast - validation_data) ** 2)
}
}
```

This is also similar to what `auto.arima()`

does; by default, the `max.order`

argument in `auto.arima()`

is set to 5, and is the sum of `p + q + P + Q`

. So I’ll try something similar.

Let’s take a look at the cost at the parameter values returned by `auto.arima()`

:

```
cost_function_rmse_low_k(c(1, 0, 2, 2, 1, 0),
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = 65,
max.order = 5)
```

`## [1] 0.1731473`

Let’s see what will happen:

```
cl <- makePSOCKcluster(8)
clusterExport(cl, c('logged_train_data', 'logged_validation_data'))
tic <- Sys.time()
auto_arima_rmse_low_k <- genoud(cost_function_rmse_low_k,
nvars = 6,
data.type.int = TRUE,
starting.values = c(1, 0, 2, 2, 1, 0), # <- from auto.arima
max.order = 5,
Domains = domains,
cluster = cl,
train_data = logged_train_data,
validation_data = logged_validation_data,
forecast_periods = length(logged_validation_data),
hard.generation.limit = TRUE)
toc_rmse_low_k <- Sys.time() - tic
```

It took 1 minute to train this one, quite fast! Let’s take a look:

`auto_arima_rmse_low_k`

```
## $value
## [1] 0.002503478
##
## $par
## [1] 1 2 0 3 1 0
##
## $gradients
## [1] NA NA NA NA NA NA
##
## $generations
## [1] 11
##
## $peakgeneration
## [1] 1
##
## $popsize
## [1] 1000
##
## $operators
## [1] 122 125 125 125 125 126 125 126 0
```

And let’s plot it:

```
best_model_rmse_low_k <- arima(logged_train_data, order = auto_arima_rmse_low_k$par[1:3],
season = list(order = auto_arima_rmse_low_k$par[4:6], period = 12),
method = "ML")
summary(best_model_rmse_low_k)
```

```
##
## Call:
## arima(x = logged_train_data, order = auto_arima_rmse_low_k$par[1:3], seasonal = list(order = auto_arima_rmse_low_k$par[4:6],
## period = 12), method = "ML")
##
## Coefficients:
## ar1 sar1 sar2 sar3
## -0.6468 -0.7478 -0.5263 -0.1143
## s.e. 0.0846 0.1171 0.1473 0.1446
##
## sigma^2 estimated as 0.01186: log likelihood = 57.88, aic = -105.76
##
## Training set error measures:
## ME RMSE MAE MPE MAPE
## Training set 0.0005953302 0.1006917 0.06165919 0.003720452 0.5291736
## MASE ACF1
## Training set 0.6442205 -0.3706693
```

```
best_model_rmse_low_k_forecast <- forecast::forecast(best_model_rmse_low_k, h = 65)
best_model_rmse_low_k_forecast <- to_tibble(best_model_rmse_low_k_forecast)
```

```
## Joining, by = "date"
## Joining, by = "date"
```

```
avia_clean_monthly %>%
group_by(date) %>%
summarise(total = sum(passengers)) %>%
mutate(total_ihs = ihs(total)) %>%
ggplot() +
ggtitle("Minimization of RMSE + low k") +
geom_line(aes(y = total_ihs, x = date), colour = "#82518c") +
scale_x_date(date_breaks = "1 year", date_labels = "%m-%Y") +
geom_ribbon(data = best_model_rmse_low_k_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#5160a0", alpha = 0.2) +
geom_line(data = best_model_rmse_low_k_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#208480") +
geom_ribbon(data = starting_model_forecast, aes(x = date, ymin = lower95, ymax = upper95),
fill = "#98431e", alpha = 0.2) +
geom_line(data = starting_model_forecast, aes(x = date, y = point_estimate),
linetype = 2, colour = "#a53031") +
theme_blog()
```

Looks like this was not the right strategy. There might be a better cost function than what I have tried, but looks like minimizing the BIC is the way to go.

Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates or buy me an espresso.