`check_short_attenuation()` detects periods of temporary signal attenuation by the upward (or downward) peak position of the smoothed average (or standard deviation) time series.
check_short_attenuation(
vctr_time,
vctr_z,
vctr_avg,
vctr_sd,
wndw_size_conv = 48 * 15,
inv_sigma_conv = 0.01,
label_err = -9999
)A data frame with columns below:
* The first column, `time_start`, gives the timestamp at which the detected attenuation period begins.
* The second column, `time_peak`, gives the timestamp of the detected attenuation period peak.
* The third column, `time_end`, gives the timestamp at which the detected attenuation period ends.
* The fourth column, `ratio`, gives the average of the ratio of the standard deviation at the detected attenuation peak to that at the beginning and end of the attenuation period. This value can be used as a threshold for determining the final attenuation periods that require Z-score modification.
A timestamp vector of class POSIXct or POSIXt. This vector indicates the timings of the end of each measurement in local time. Any interval (typically 15 to 60 min) is allowed, but the timestamps must be equally spaced and arranged chronologically.
A vector of Z-score time series. The length of the vector must match that of the timestamp vector.
A vector of moving window average time series that used in transforming the original time series into the Z-score time series. The length of the vector must match that of the timestamp vector. The unit of the time series must match that of `vctr_sd`.
A vector of moving window standard deviation time series that used in transforming the original time series into the Z-score time series. The length of the vector must match that of the timestamp vector. The unit of the time series must match that of `vctr_avg`.
A positive integer indicating the number of data points included in a moving window. THeefault is 48 * 15, meaning that the window size is 15 days if the time interval of the input timestamp is 30 minutes.
A positive value defining a Gaussian window width. The width of the Gaussian window is inversely proportional to this parameter. Default is 0.01.
A numeric value representing a missing value in the input vector(s). Default is -9999.
Yoshiaki Hata
In some cases, the dT time series may yield a signal that is attenuated for only a short period, for example, when rainfall continues for days, causing the moving window mean (or standard deviation) to increase (or decrease). In such cases, normalization will cause the Z-score time series immediately before and after the rainfall to be unnaturally distorted, hindering the construction of the random-forest model. This function can detect periods when the moving window average has an upward peak, and the moving window standard deviation has a downward peak simultaneously.
First, the average and standard deviation time series are interpolated if they contain missing values. Second, they are smoothed by convolution with a Gaussian window specified by the user through the parameters `wndw_size_conv` and `inv_sigma_conv`. Third, the first-order and second-order differences of both smoothed time series are calculated, which determine the upward peak positions of the average and the downward peak positions of the standard deviation. Finally, possible signal attenuation periods are output. The start and end of the periods are defined by the timings when the first-order differenced standard deviation time series changes its sign before and after each peak.