anomaly_memory_time_min
Anomaly score calculator parameter: Memory time for
abnormal situation.

1–600 
45 
double 
CI/Metric 
excess_z_score
Anomaly score calculator parameter: Minimal anomalousness
accumulated for outlier.

0–3 
0.8 
double 
CI/Metric 
linear_accumulator_threshold
Decision Tree Threshold: ACCUMULATOR analysis

0.5–5 
1 
double 
Global 
low_freq_power_threshold
Decision Tree Threshold: WEEKLY analysis

0–100 
50 
double 
Global 
low_variability_threshold
Decision Tree Threshold: TRENDY analysis

0.0000000001–0.001 
0.0001 
double 
Global 
mid_freq_power_threshold
Decision Tree Threshold: WEEKLY analysis

0–100 
33 
double 
Global 
multinomial_count_threshold
Decision Tree Threshold: MULTINOMIAL analysis

1–1000 
40 
integer 
Global 
non_zero_diff_threshold
Decision Tree Threshold: NEAR_CONSTANT analysis

0–100 
5 
double 
Global 
normal_memory_time_min
Anomaly score calculator parameter: Memory time for
normal situation.

1–600 
1 
double 
CI/Metric 
normal_probability_ewma_timescale_min
Anomaly score calculator parameter: Normal assessment
timescale.

1–600 
15 
double 
CI/Metric 
normal_probability_weight
Anomaly score calculator parameter: Normal assessment
adjustment factor.

0–1 
1 
double 
CI/Metric 
sigmoid_offset
Anomaly score calculator parameter: Anomalousness to
score conversion.

0–5 
2.1 
double 
CI/Metric 
sigmoid_weight
Anomaly score calculator parameter: Anomalousness to
score conversion.

0–5 
1.2 
double 
CI/Metric 
tiny_variability_threshold
Decision Tree Threshold: NEAR_CONSTANT analysis

0–0.001 
0.0000000001 
double 
Global 
weekly_peak_hi_limit
Decision Tree Threshold: WEEKLY analysis.

7–14 
10 
double 
Global 
weekly_peak_lo_limit
Decision Tree Threshold: Weekly analysis.

0.5–7 
0.7 
double 
Global 
weekly_vs_daily_log_likelihood_threshold
By how much log likelihood of weekly needs to be larger
than daily, to be the preferred statistical model.

100–1000 
200 
double 
CI/Metric

daily_vs_noisy_log_likelihood_threshold
By how much log likelihood of daily needs to be larger
than noisy, to be the preferred statistical model.

20–1000 
200 
double 
CI/Metric

weekly_vs_noisy_log_likelihood_treshold
By how much log likelihood of weekly needs to be larger
than noisy, to be the preferred statistical model.

100–1000 
200 
double 
CI/Metric

trendy_vs_noisy_log_likelihood_threshold
By how much log likelihood of trendy needs to be larger
than noisy, to be the preferred statistical model.

10–1000 
50 
double 
CI/Metric

seasonal_loess_width_in_hours
Applied to the seasonal component of a weekly or daily
model before making a forecast of future behavior. If
set to 0, each data point in the seasonal model becomes
independent of the rest of the data points.

6–24 
12 
double 
CI/Metric

robustness
Affects how outliers contribute to seasonal and trend
calculations.

NA 
true 
boolean 
CI/Metric

snpm_min_value_threshold
Minimum value of data required for building an SNPM
model.

1e9 – 1e9 
0 
double 
CI/Metric

snpm_max_observation_interval_in_sec
Maximum expected observation interval required for
building an SNPM model.

60 – 600000 
120 
integer 
CI/Metric

min_std_jump_fraction
Minimum ratio of locally calculated observation noise
level to typical jump size that justifies recalculating
a larger observation noise variance.

0.0 – 1.0 
0.2 
double 
CI/Metric

dynamic_threshold_error_smoothing
Whether to use exponentially weighted moving average to
smooth the residuals in the dynamic threshold
analysis.

NA 
true 
boolean 
CI/Metric

ewma_alpha
The alpha value of the exponentially weighted moving
average in dynamic threshold analysis.

1e15 – 1.0 
0.02739726027 
double 
CI/Metric

dynamic_threshold_beginning_smoothing_length
Number of smoothed data points to set to the mean of
double the smoothing length.

0 – 10000 
250 
integer 
CI/Metric

dynamic_threshold_error_buffer_minutes
Number of data points around each outlier to group
together.

1 – 1000 
30 
integer 
CI/Metric

dynamic_threshold_search_start
Start value at which the optimal control factor is looked
for.

0.5 – 20.0 
3.0

double 
CI/Metric

dynamic_threshold_search_interval
Interval between search values of optimal control
factor.

0.1 – 5.0 
0.5 
double 
CI/Metric

dynamic_threshold_search_count
Number of values required for searching for optimal
control factor.

1 – 50 
19 
integer 
CI/Metric

dynamic_threshold_error_sequence_limit
Maximum number of error groups for a particular control
factor value when searching.

1 – 20 
5 
integer 
CI/Metric

dynamic_threshold_minimum_data_count
Minimum number of raw data points needed before
attempting dynamic thresholding.

1 – 10000 
5000 
integer 
CI/Metric

linear_seasonal_log_likelihood_threshold
Threshold used in deciding whether to prefer a fitted
model with linear seasonality over a model with a
periodic component.

105000 
1000 
integer 
CI/Metric
