useR! 2024

Large-scale time series forecasting competitions are excellent platforms for fostering innovation and advancing the field of time series analysis. One of the most frequent problems that arises in forecasting competitions is data leakage. Data leaks can happen when the training period values contain information about the test period values. 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 ; iv) addition of white noise; v) modified scales; vi) temporal aggregations: for example, create monthly series using daily series, etc. The tsdataleaks package provides a simple and computationally efficient algorithm to exploit data leaks in time series data.  The tsdataleaks package is available on CRAN.