What factors contribute to data leakage in time series observations for meteorological conditions?
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Data leakage in time series observations for meteorological conditions can occur due to various factors. One significant factor is the presence of outliers and inconsistencies in the observed time series, which can be influenced by meteorological factors like snow coverage. Additionally, the use of pulse compression techniques in meteorological radars can lead to artifacts like range sidelobes, causing information to spread across multiple range gates and potentially contaminating data. Moreover, the characteristics of time series data, such as high noise levels and non-stationarity, can contribute to data leakage issues in prediction methods, potentially leading to using testing set information in the training process. Addressing these factors is crucial to ensure accurate and reliable predictions in meteorological time series analysis.
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| Papers (4) | Insight |
|---|---|
| Factors contributing to data leakage in meteorological time series are massive missingness, varying missing rates, and missing not at random (MNAR) mechanisms, addressed by the proposed FBVS-LSTM model. | |
| Factors contributing to data leakage in time series observations for meteorological conditions include high noise, non-stationarity, and nonlinear characteristics, leading to information from the testing set being used in training. | |
Open access 01 Jan 2007 | Snow coverage on GPS antennas during extreme winter conditions leads to outliers and inconsistencies in time series observations, affecting data quality for meteorological conditions in geodetic studies. |
| Factors contributing to data leakage in time series observations for meteorological conditions include cloud cover obstructing satellite data collection, necessitating aggregation and spatial interpolation methods for accurate analysis. |
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