pyacs.gts.lib.l1trend.outlier_flagging module
Outlier flagging using L1-trend analysis.
This module provides functions to identify and flag outliers in GPS time series based on their deviation from L1-trend filtered representations.
- pyacs.gts.lib.l1trend.outlier_flagging.flag_outliers_using_l1trend(self, l1trend, threshold=5)[source]
Flag outliers in the time series based on a user provided l1trend filter representation of the time series.
This function identifies outliers by comparing the original time series to its L1-trend representation. Outliers are flagged based on their deviation from the trend using the Median Absolute Deviation (MAD) method.
- Parameters:
self (Gts object) – The time series to flag outliers in.
l1trend (Gts object) – The l1trend representation of the time series.
threshold (float, optional) – The threshold for flagging outliers. Default is 5 for 5 times the median absolute deviation (MAD) from the median.
- Returns:
The original time series object with outliers flagged in self.outliers
- Return type:
Notes
The method uses the Median Absolute Deviation (MAD) which is more robust to outliers than standard deviation. Outliers are identified as points that deviate more than threshold * MAD from the median of the absolute differences between the original time series and the L1-trend representation.