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}
Split-apply-combine for Maximum Likelihood Estimation of a linear model
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
{disk.frame} is epic
{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

I have recently been confronted to a kind of data set and problem that I was not even aware existed:
intermittent demand data. Intermittent demand arises when the demand for a certain good arrives
sporadically. Let’s take a look at an example, by analyzing the number of downloads for the `{RDieHarder}`

package:

```
library(tidyverse)
library(tsintermittent)
library(nnfor)
library(cranlogs)
library(brotools)
```

```
rdieharder <- cran_downloads("RDieHarder", from = "2017-01-01")
ggplot(rdieharder) +
geom_line(aes(y = count, x = date), colour = "#82518c") +
theme_blog()
```

Let’s take a look at just one month of data, because the above plot is not very clear, because of the outlier just before 2019… I wonder now, was that on Christmas day?

```
rdieharder %>%
filter(count == max(count))
```

```
## date count package
## 1 2018-12-21 373 RDieHarder
```

Not exactly on Christmas day, but almost! Anyways, let’s look at one month of data:

```
january_2018 <- rdieharder %>%
filter(between(date, as.Date("2018-01-01"), as.Date("2018-02-01")))
ggplot(january_2018) +
geom_line(aes(y = count, x = date), colour = "#82518c") +
theme_blog()
```

Now, it is clear that this will be tricky to forecast. There is no discernible pattern, no trend, no seasonality… nothing that would make it “easy” for a model to learn how to forecast such data.

This is typical intermittent demand data. Specific methods have been developed to forecast such
data, the most well-known being Croston, as detailed in
this paper.
A function to estimate such models is available in the `{tsintermittent}`

package, written by
Nikolaos Kourentzes
who also wrote another package, `{nnfor}`

, which uses Neural Networks to forecast time series data.
I am going to use both to try to forecast the intermittent demand for the `{RDieHarder}`

package
for the year 2019.

Let’s first load these packages:

```
library(tsintermittent)
library(nnfor)
```

And as usual, split the data into training and testing sets:

```
train_data <- rdieharder %>%
filter(date < as.Date("2019-01-01")) %>%
pull(count) %>%
ts()
test_data <- rdieharder %>%
filter(date >= as.Date("2019-01-01"))
```

Let’s consider three models; a naive one, which simply uses the mean of the training set as the
forecast for all future periods, Croston’s method, and finally a Neural Network from the `{nnfor}`

package:

```
naive_model <- mean(train_data)
croston_model <- crost(train_data, h = 163)
nn_model <- mlp(train_data, reps = 1, hd.auto.type = "cv")
```

```
## Warning in preprocess(y, m, lags, keep, difforder, sel.lag,
## allow.det.season, : No inputs left in the network after pre-selection,
## forcing AR(1).
```

`nn_model_forecast <- forecast(nn_model, h = 163)`

The `crost()`

function estimates Croston’s model, and the `h`

argument produces the
forecast for the next 163 days. `mlp()`

trains a multilayer perceptron, and the `hd.auto.type = "cv"`

argument means that 5-fold cross-validation will be used to find the best number of hidden nodes. I
then obtain the forecast using the `forecast()`

function. As you can read from the Warning message
above, the Neural Network was replaced by an auto-regressive model, AR(1), because no inputs were
left after pre-selection… I am not exactly sure what that means, but if I remove the big outlier
from before, this warning message disappears, and a Neural Network is successfully trained.

In order to rank the models, I follow this paper from Rob J. Hyndman, who wrote a very useful book titled Forecasting: Principles and Practice, and use the Mean Absolute Scaled Error, or MASE. You can also read this shorter pdf which also details how to use MASE to measure the accuracy for intermittent demand. Here is the function:

```
mase <- function(train_ts, test_ts, outsample_forecast){
naive_insample_forecast <- stats::lag(train_ts)
insample_mae <- mean(abs(train_ts - naive_insample_forecast), na.rm = TRUE)
error_outsample <- test_ts - outsample_forecast
ase <- error_outsample / insample_mae
mean(abs(ase), na.rm = TRUE)
}
```

It is now easy to compute the models’ accuracies:

`mase(train_data, test_data$count, naive_model)`

`## [1] 1.764385`

`mase(train_data, test_data$count, croston_model$component$c.out[1])`

`## [1] 1.397611`

`mase(train_data, test_data$count, nn_model_forecast$mean)`

`## [1] 1.767357`

Croston’s method is the one that performs best from the three. Maybe surprisingly, the naive method performs just as well as the Neural Network! (or rather, the AR(1) model) Let’s also plot the predictions with the true values from the test set:

```
test_data <- test_data %>%
mutate(naive_model_forecast = naive_model,
croston_model_forecast = croston_model$component$c.out[1],
nn_model_forecast = nn_model_forecast$mean) %>%
select(-package) %>%
rename(actual_value = count)
test_data_longer <- test_data %>%
gather(models, value,
actual_value, naive_model_forecast, croston_model_forecast, nn_model_forecast)
```

```
## Warning: attributes are not identical across measure variables;
## they will be dropped
```

```
ggplot(test_data_longer) +
geom_line(aes(y = value, x = date, colour = models)) +
theme_blog()
```

Just to make sure I didn’t make a mistake when writing the `mase()`

function, let’s use the
`accuracy()`

function from the `{forecast}`

package and compare the result for the Neural Network:

```
library(forecast)
accuracy(nn_model_forecast, x = test_data$actual_value)
```

```
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set 0.001929409 14.81196 4.109577 NaN Inf 0.8437033 0.05425074
## Test set 8.211758227 12.40199 8.635563 -Inf Inf 1.7673570 NA
```

The result is the same, so it does seem like the naive method is not that bad, actually! Now, in general, intermittent demand series have a lot of 0 values, which is not really the case here. I still think that the methodology fits to this particular data set.

How else would you have forecast this data? Let me know via twitter!

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