Contents IT Operations Management Previous Topic Next Topic Create configuration settings rule Subscribe Log in to subscribe to topics and get notified when content changes. ... SAVE AS PDF Selected Topic Topic & Subtopics All Topics in Contents Share Create configuration settings rule Configuration settings affect how metric data is processed. Configuration settings rules override the default values of these configuration settings, letting you build efficient statistical models for different CI classes. Before you beginRole required: evt_mgmt_admin About this taskA number of configuration settings determine the behavior of Operational Intelligence MID Servers. In the base system, these configuration settings are configured with default values, data types, and range of valid values. You cannot directly modify these configuration settings or add new ones. However, you can create a metric configuration rule with new configuration settings that override the default values on the MID Servers. Then, manually apply these rules to all Operational Intelligence MID Servers in a single synchronization operation, or rely on an hourly system job to perform the synchronization.To take effect, Operational Intelligence MID Servers must be synchronized with the updates to the configuration settings rules.It is valid to have multiple rules for a setting that affect the same CIs, in which case: Rules in which a filter is defined take precedence over a global rule in which no filter has been defined. If multiple rules that affect the same set of CIs have the same priority, then only the latest rule to be defined is applied. If multiple rules with different priorities affect the same set of CIs, then rules with the highest priority are applied. Procedure Navigate to Operational Intelligence > Configure > Metric Config Rules. On the Metric Configuration Rules pane, click New, and fill out the form. Table 1. Metric Configuration Rules form Field Description Name Rule name. Order Rule priority within all other rules. Higher numbers represent higher priorities. Filter by Check box for displaying the Rule field, where you can specify conditions that CIs must meet for the rule to apply. For example, in the choose field list, select Add Related Fields and then add the filter [class][is][Linux Server]. If clear, the rule applies globally to all CIs in the Metric To CI Mapping [sa_metric_map] table. Right-click the form title, and click Save. In the Metric overridden configurations form section click New, fill out the form, and then click Submit. Table 2. Metric overridden configurations form Field Description Name Configuration setting for which to override its value. Click the icon to display the list of all configuration settings.Click the icon to display the Metric Settings dialog with details such as range of possible values.See the following tables (Configuration Settings and Internal Configuration Settings) for details about configuration settings. Rule Rule to which the created configuration setting applies. Value New value that overrides the default value for the specified configuration setting. You can modify the following configuration settings in the Name field.Note: The filter specified in the metric configuration rule does not apply to settings with a global scope. Table 3. Configuration Settings Name and Description Values Default Data Type Scope anomaly_detection_enabled Enable/disable anomaly detection. Note: If anomaly_detection_action_level is set, then anomaly_detection_enabled is ignored. NA true boolean CI/Metric anomaly_detection_action_level Action level of anomaly analysis and processing. For more information, see Configure the action level of anomaly detection. choices: Metrics Only Bounds Anomaly Scores Anomaly Alerts IT Alerts New records: Bounds Upgraded records: Anomaly Alerts choice CI/Metric buffer_anomaly_eviction_size Maximum number of anomalies at individual metric level that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global buffer_ci_score_eviction_size Maximum number of anomalies at CI level that can be stored in internal buffer before sending them to instance (Currently not being used) 60–1440 60 integer Global buffer_metric_eviction_size Maximum number of metrics that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global connection_login_timeout_secs Maximum time in seconds to log in to the local database on MID Server. 30–60 30 integer Global corrupt_data_count_threshold Minimum number of training points (15-minute averages) required to do any statistical analysis. 10–100 30 integer Global deprioritize_early_batching_of_anomalous_ci Send anomalous CI information immediately or at regular interval. NA false boolean Global max_pool_connections_sizeMaximum number of connections for local database pool. 10–50 25 integer Global observation_time_min Expected minimum metric observation interval. 1–1440 1 integer CI/Metric robust_central_percentage Percentage of the residual data to compute the residual standard deviation, used for outlier detection. When set to 100 - uses the regular sample standard deviation. 50–100 90 double Global sparse_gap_fraction_thresholdIf more than this percentage of data is missing and no other class has been identified, classify as SPARSE. Do not attempt to fit a WEEKLY model. 0–100 50 double Global weekly_model_min_daysNumber of days for which data must be available in order to consider only a WEEKLY seasonality decomposition. 14-90 15 integer CI/Metric daily_model_min_daysNumber of days for which data must be available in order to consider only a DAILY seasonality decomposition. 2-90 3 integer CI/Metric build_snpm_modelEnable/disable building an SNPM data model. NA true boolean CI/Metric snpm_minimum_data_count Minimum number of data points required for building a stationary nonparametric model. 0 – 1e9 5000 integer CI/Metric The following configuration settings are for internal usage. Table 4. Internal Configuration Settings Name and Description Values Default Data Type Scope 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 time-scale. 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. 1e-15 – 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. 10-5000 1000 integer CI/Metric On this page Send Feedback Previous Topic Next Topic
Create configuration settings rule Configuration settings affect how metric data is processed. Configuration settings rules override the default values of these configuration settings, letting you build efficient statistical models for different CI classes. Before you beginRole required: evt_mgmt_admin About this taskA number of configuration settings determine the behavior of Operational Intelligence MID Servers. In the base system, these configuration settings are configured with default values, data types, and range of valid values. You cannot directly modify these configuration settings or add new ones. However, you can create a metric configuration rule with new configuration settings that override the default values on the MID Servers. Then, manually apply these rules to all Operational Intelligence MID Servers in a single synchronization operation, or rely on an hourly system job to perform the synchronization.To take effect, Operational Intelligence MID Servers must be synchronized with the updates to the configuration settings rules.It is valid to have multiple rules for a setting that affect the same CIs, in which case: Rules in which a filter is defined take precedence over a global rule in which no filter has been defined. If multiple rules that affect the same set of CIs have the same priority, then only the latest rule to be defined is applied. If multiple rules with different priorities affect the same set of CIs, then rules with the highest priority are applied. Procedure Navigate to Operational Intelligence > Configure > Metric Config Rules. On the Metric Configuration Rules pane, click New, and fill out the form. Table 1. Metric Configuration Rules form Field Description Name Rule name. Order Rule priority within all other rules. Higher numbers represent higher priorities. Filter by Check box for displaying the Rule field, where you can specify conditions that CIs must meet for the rule to apply. For example, in the choose field list, select Add Related Fields and then add the filter [class][is][Linux Server]. If clear, the rule applies globally to all CIs in the Metric To CI Mapping [sa_metric_map] table. Right-click the form title, and click Save. In the Metric overridden configurations form section click New, fill out the form, and then click Submit. Table 2. Metric overridden configurations form Field Description Name Configuration setting for which to override its value. Click the icon to display the list of all configuration settings.Click the icon to display the Metric Settings dialog with details such as range of possible values.See the following tables (Configuration Settings and Internal Configuration Settings) for details about configuration settings. Rule Rule to which the created configuration setting applies. Value New value that overrides the default value for the specified configuration setting. You can modify the following configuration settings in the Name field.Note: The filter specified in the metric configuration rule does not apply to settings with a global scope. Table 3. Configuration Settings Name and Description Values Default Data Type Scope anomaly_detection_enabled Enable/disable anomaly detection. Note: If anomaly_detection_action_level is set, then anomaly_detection_enabled is ignored. NA true boolean CI/Metric anomaly_detection_action_level Action level of anomaly analysis and processing. For more information, see Configure the action level of anomaly detection. choices: Metrics Only Bounds Anomaly Scores Anomaly Alerts IT Alerts New records: Bounds Upgraded records: Anomaly Alerts choice CI/Metric buffer_anomaly_eviction_size Maximum number of anomalies at individual metric level that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global buffer_ci_score_eviction_size Maximum number of anomalies at CI level that can be stored in internal buffer before sending them to instance (Currently not being used) 60–1440 60 integer Global buffer_metric_eviction_size Maximum number of metrics that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global connection_login_timeout_secs Maximum time in seconds to log in to the local database on MID Server. 30–60 30 integer Global corrupt_data_count_threshold Minimum number of training points (15-minute averages) required to do any statistical analysis. 10–100 30 integer Global deprioritize_early_batching_of_anomalous_ci Send anomalous CI information immediately or at regular interval. NA false boolean Global max_pool_connections_sizeMaximum number of connections for local database pool. 10–50 25 integer Global observation_time_min Expected minimum metric observation interval. 1–1440 1 integer CI/Metric robust_central_percentage Percentage of the residual data to compute the residual standard deviation, used for outlier detection. When set to 100 - uses the regular sample standard deviation. 50–100 90 double Global sparse_gap_fraction_thresholdIf more than this percentage of data is missing and no other class has been identified, classify as SPARSE. Do not attempt to fit a WEEKLY model. 0–100 50 double Global weekly_model_min_daysNumber of days for which data must be available in order to consider only a WEEKLY seasonality decomposition. 14-90 15 integer CI/Metric daily_model_min_daysNumber of days for which data must be available in order to consider only a DAILY seasonality decomposition. 2-90 3 integer CI/Metric build_snpm_modelEnable/disable building an SNPM data model. NA true boolean CI/Metric snpm_minimum_data_count Minimum number of data points required for building a stationary nonparametric model. 0 – 1e9 5000 integer CI/Metric The following configuration settings are for internal usage. Table 4. Internal Configuration Settings Name and Description Values Default Data Type Scope 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 time-scale. 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. 1e-15 – 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. 10-5000 1000 integer CI/Metric
Create configuration settings rule Configuration settings affect how metric data is processed. Configuration settings rules override the default values of these configuration settings, letting you build efficient statistical models for different CI classes. Before you beginRole required: evt_mgmt_admin About this taskA number of configuration settings determine the behavior of Operational Intelligence MID Servers. In the base system, these configuration settings are configured with default values, data types, and range of valid values. You cannot directly modify these configuration settings or add new ones. However, you can create a metric configuration rule with new configuration settings that override the default values on the MID Servers. Then, manually apply these rules to all Operational Intelligence MID Servers in a single synchronization operation, or rely on an hourly system job to perform the synchronization.To take effect, Operational Intelligence MID Servers must be synchronized with the updates to the configuration settings rules.It is valid to have multiple rules for a setting that affect the same CIs, in which case: Rules in which a filter is defined take precedence over a global rule in which no filter has been defined. If multiple rules that affect the same set of CIs have the same priority, then only the latest rule to be defined is applied. If multiple rules with different priorities affect the same set of CIs, then rules with the highest priority are applied. Procedure Navigate to Operational Intelligence > Configure > Metric Config Rules. On the Metric Configuration Rules pane, click New, and fill out the form. Table 1. Metric Configuration Rules form Field Description Name Rule name. Order Rule priority within all other rules. Higher numbers represent higher priorities. Filter by Check box for displaying the Rule field, where you can specify conditions that CIs must meet for the rule to apply. For example, in the choose field list, select Add Related Fields and then add the filter [class][is][Linux Server]. If clear, the rule applies globally to all CIs in the Metric To CI Mapping [sa_metric_map] table. Right-click the form title, and click Save. In the Metric overridden configurations form section click New, fill out the form, and then click Submit. Table 2. Metric overridden configurations form Field Description Name Configuration setting for which to override its value. Click the icon to display the list of all configuration settings.Click the icon to display the Metric Settings dialog with details such as range of possible values.See the following tables (Configuration Settings and Internal Configuration Settings) for details about configuration settings. Rule Rule to which the created configuration setting applies. Value New value that overrides the default value for the specified configuration setting. You can modify the following configuration settings in the Name field.Note: The filter specified in the metric configuration rule does not apply to settings with a global scope. Table 3. Configuration Settings Name and Description Values Default Data Type Scope anomaly_detection_enabled Enable/disable anomaly detection. Note: If anomaly_detection_action_level is set, then anomaly_detection_enabled is ignored. NA true boolean CI/Metric anomaly_detection_action_level Action level of anomaly analysis and processing. For more information, see Configure the action level of anomaly detection. choices: Metrics Only Bounds Anomaly Scores Anomaly Alerts IT Alerts New records: Bounds Upgraded records: Anomaly Alerts choice CI/Metric buffer_anomaly_eviction_size Maximum number of anomalies at individual metric level that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global buffer_ci_score_eviction_size Maximum number of anomalies at CI level that can be stored in internal buffer before sending them to instance (Currently not being used) 60–1440 60 integer Global buffer_metric_eviction_size Maximum number of metrics that can be stored in internal buffer before sending them to instance for every CI/Metric pair. 60–1440 60 integer Global connection_login_timeout_secs Maximum time in seconds to log in to the local database on MID Server. 30–60 30 integer Global corrupt_data_count_threshold Minimum number of training points (15-minute averages) required to do any statistical analysis. 10–100 30 integer Global deprioritize_early_batching_of_anomalous_ci Send anomalous CI information immediately or at regular interval. NA false boolean Global max_pool_connections_sizeMaximum number of connections for local database pool. 10–50 25 integer Global observation_time_min Expected minimum metric observation interval. 1–1440 1 integer CI/Metric robust_central_percentage Percentage of the residual data to compute the residual standard deviation, used for outlier detection. When set to 100 - uses the regular sample standard deviation. 50–100 90 double Global sparse_gap_fraction_thresholdIf more than this percentage of data is missing and no other class has been identified, classify as SPARSE. Do not attempt to fit a WEEKLY model. 0–100 50 double Global weekly_model_min_daysNumber of days for which data must be available in order to consider only a WEEKLY seasonality decomposition. 14-90 15 integer CI/Metric daily_model_min_daysNumber of days for which data must be available in order to consider only a DAILY seasonality decomposition. 2-90 3 integer CI/Metric build_snpm_modelEnable/disable building an SNPM data model. NA true boolean CI/Metric snpm_minimum_data_count Minimum number of data points required for building a stationary nonparametric model. 0 – 1e9 5000 integer CI/Metric The following configuration settings are for internal usage. Table 4. Internal Configuration Settings Name and Description Values Default Data Type Scope 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 time-scale. 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. 1e-15 – 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. 10-5000 1000 integer CI/Metric