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A high-level python package integrating expert knowledge and artificial intelligence to boost sub-seasonal to seasonal (S2S) forecasting.
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Lilio
A Python package for generating calendars to resample timeseries into training and target data for machine learning. Named after the inventor of the Gregorian Calendar.
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What Lilio can do for you
Lilio helps you to organize timeseries data in such a way that it can easily be used in a machine learning workflow. It is geared towards predictions of events with (annual) return periods. Lilio distinguishes between target and precursor periods (features) and stacks the timeseries such that targets from different years are treated as independent records (samples).
Concretely, Lilio helps you to:
- Define target and precursor periods
- Resample and reshape input data
- Split data into train-test sets
- Avoid information leakage between train/test sets
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Reference papers
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1Other
2- 1.Author(s): Yang Liu, Bart Schilperoort, Peter Kalverla, Sem Vijverberg, Jannes van Ingen, Sarah Alidoost, Stefen Verhoeven, Dim CoumouPublished by Copernicus GmbH in 202310.5194/ems2023-501
- 2.Author(s): Yang Liu, Bart Schilperoort, Jannes van Ingen, Sem Vijverberg, Peter Kalverla, Dim CoumouPublished by Copernicus GmbH in 202310.5194/egusphere-egu23-953
Contributors
Contact person
Yang Liu
SV
Sem Vijverberg
Vrije Universiteit Amsterdam
JvI
Jannes van Ingen
Vrije Universiteit Amsterdam
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