The dailyDCOffsetMetric() function identifies days with a jump in the signal mean.

1 2 3 4 5 | ```
dailyDCOffsetMetric(df,
offsetDays=5,
outlierWindow=7,
outlierThreshold=3.0,
outputType=1)
``` |

`df` |
a dataframe containing |

`offsetDays` |
number of days used in calculating weighting factors |

`outlierWindow` |
window size passed to findOutliers() function in the seismic package |

`outlierThreshold` |
detection threshold passed to findOutliers() function in the seismic package |

`outputType` |
if 1, return last day of valid values (index= length(index)-floor(outlierWindow/2)); if 0, return all valid values (indices= max(offsetDays, floor(outlierWindow/2): length(index)-floor(outlierWindow/2)) |

This algorithm calculates lagged differences of the daily mean timeseries over a window of `offsetDays`

days.
Shifts in the mean that are persistent and larger than the typical standard deviation of daily means will
generate higher metric values.

Details of the algorithm are as follows

1 2 3 4 5 6 7 | ```
# data0 = download requested daily means (in the 'df' dataframe), must be greater than max(offsetDays,outlierWindow)+floor(outlierWindow/2)
# data1 = remove outliers using MAD outlier detection with the 'outlier' arguments specified
# data2 = replace outliers with rolling median values using a default 7 day window, remove last floor(outlierWindow/2) number of samples.
# weights = calculate absolute lagged differences with 1-N day lags, default N=5
# metric0 = multiply the lagged differences together and take the N'th root
# stddev0 = calculate the rolling standard deviation of data2 with a N-day window
# METRIC = divide metric0 by the median value of stddev0
``` |

A list is returned with a `SingleValueMetric`

object for the last day-floor(outlierWindow/2) (default 3rd from last day) in the incoming dataframe if outputType=1 (one list element), otherwise the first+offsetDays to last day-floor(outlierWindow/2) (multiple list elements, one per day) if outputType=0.

Prefer 60+ days of sample_mean values to get a good estimate of the long term sample_mean standard deviation. After initial testing on stations in the IU network, a metric value > 10 appears to be indicative of a DC offset shift (this may vary across stations or networks and larger values may be preferred as indications of a potential station issue).

Jonathan Callahan jonathan@mazamascience.com

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