  Forecasting competitions are of increasing importance as a means to learn
best practices and gain knowledge. Data leakage is one of the most common
issues that can often be found in competitions. Data leaks can happen when the
training data contains information about the test data. There are a variety of
different ways that data leaks can occur with time series data. For example: i)
randomly chosen blocks of time series are concatenated to form a new time
series; ii) scale-shifts; iii) repeating patterns in time series; iv) white
noise is added to the original time series to form a new time series, etc. This
work introduces a novel tool to detect these data leaks. The tsdataleaks
package provides a simple and computationally efficient algorithm to exploit
data leaks in time series data. This paper demonstrates the package design and
its power to detect data leakages with an application to forecasting
competition data.
