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  "Title": "Exploit Data Leakages in Time Series Forecasting Competitions",
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  "Description": "Forecasting competitions are of increasing importance as a\nmean to learn best practices and gain knowledge. Data leakage\nis one of the most common issues that can often be found in\ncompetitions. Data leaks can happen when the training data\ncontains information about the test data. For example: randomly\nchosen blocks of time series are concatenated to form a new\ntime series, scale-shifts, repeating patterns in time series,\nwhite noise is added in the original time series to form a new\ntime series, etc.  'tsdataleaks' package can be used to detect\ndata leakages in a collection of time series.",
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