init

correlation ¤

Correlation Events

Cross-signal correlation analysis for detecting related anomalies and patterns across multiple timeseries signals.

SignalCorrelationEvents: Analyze correlations between signals.
rolling_correlation: Time-windowed Pearson correlation between two signals.
correlation_breakdown: Detect periods where normally correlated signals diverge.
lag_correlation: Cross-correlation with time lag analysis.
AnomalyCorrelationEvents: Correlate anomaly events across signals.
coincident_anomalies: Find anomalies occurring simultaneously across signals.
cascade_detection: Detect anomaly cascades (signal A anomaly followed by B).
root_cause_ranking: Rank signals by how often their anomalies precede others.

SignalCorrelationEvents ¤

SignalCorrelationEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "corr:signal",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Correlation: Signal Correlation Analysis

Analyze time-windowed correlations between pairs of numeric signals. Useful for detecting when normally correlated process variables diverge.

Methods: - rolling_correlation: Pearson correlation over rolling windows. - correlation_breakdown: Detect periods where correlation drops below threshold. - lag_correlation: Cross-correlation with time lag to find delayed relationships.

rolling_correlation ¤

rolling_correlation(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    window: int = 60
) -> pd.DataFrame

Compute rolling Pearson correlation between two signals.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal.	required
`uuid_b`	`str`	UUID of second signal.	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`window`	`int`	Rolling window size (in resampled periods).	`60`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	systime, uuid, source_uuid_a, source_uuid_b, is_delta, correlation

correlation_breakdown ¤

correlation_breakdown(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    window: int = 60,
    threshold: float = 0.5
) -> pd.DataFrame

Detect periods where correlation drops below a threshold.

Returns intervals where previously correlated signals diverge, which may indicate process issues.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal.	required
`uuid_b`	`str`	UUID of second signal.	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`window`	`int`	Rolling window size.	`60`
`threshold`	`float`	Correlation threshold below which to flag.	`0.5`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, end, uuid, source_uuid_a, source_uuid_b, is_delta, min_correlation, duration_seconds

lag_correlation ¤

lag_correlation(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    max_lag: int = 30
) -> pd.DataFrame

Cross-correlation with time lag analysis.

Finds the time lag at which two signals are most correlated.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal (reference).	required
`uuid_b`	`str`	UUID of second signal (lagged).	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`max_lag`	`int`	Maximum lag periods to test (in both directions).	`30`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	lag_periods, correlation, is_best_lag

AnomalyCorrelationEvents ¤

AnomalyCorrelationEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "corr:anomaly",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Correlation: Anomaly Correlation Analysis

Correlate anomaly events across multiple signals to find coincident patterns, cascading failures, and root cause candidates.

Methods: - coincident_anomalies: Find anomalies that co-occur within a time window. - cascade_detection: Detect anomaly cascades (A precedes B within a window). - root_cause_ranking: Rank signals by how often their anomalies precede others.

coincident_anomalies ¤

coincident_anomalies(
    signal_uuids: List[str],
    *,
    z_threshold: float = 3.0,
    coincidence_window: str = "5min",
    min_signals: int = 2
) -> pd.DataFrame

Find anomalies that co-occur across multiple signals within a time window.

Parameters:

Name	Type	Description	Default
`signal_uuids`	`List[str]`	List of signal UUIDs to analyze.	required
`z_threshold`	`float`	Z-score threshold for anomaly detection per signal.	`3.0`
`coincidence_window`	`str`	Time window for considering anomalies coincident.	`'5min'`
`min_signals`	`int`	Minimum number of signals with anomalies to flag.	`2`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	window_start, window_end, uuid, is_delta, anomaly_count, signal_uuids_involved

cascade_detection ¤

cascade_detection(
    leader_uuid: str,
    follower_uuid: str,
    *,
    z_threshold: float = 3.0,
    max_delay: str = "10min"
) -> pd.DataFrame

Detect anomaly cascades: leader anomaly followed by follower anomaly.

Identifies cases where an anomaly in signal A is followed by an anomaly in signal B within the max_delay window.

Parameters:

Name	Type	Description	Default
`leader_uuid`	`str`	UUID of the leading signal.	required
`follower_uuid`	`str`	UUID of the following signal.	required
`z_threshold`	`float`	Z-score threshold for anomaly detection.	`3.0`
`max_delay`	`str`	Maximum time between leader and follower anomaly.	`'10min'`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	leader_time, follower_time, uuid, is_delta, leader_uuid, follower_uuid, delay_seconds

root_cause_ranking ¤

root_cause_ranking(
    signal_uuids: List[str],
    *,
    z_threshold: float = 3.0,
    max_delay: str = "10min"
) -> pd.DataFrame

Rank signals by how often their anomalies precede others.

For each pair of signals, counts how many times signal A's anomaly precedes signal B's anomaly within max_delay. Signals that frequently lead are potential root causes.

Parameters:

Name	Type	Description	Default
`signal_uuids`	`List[str]`	List of signal UUIDs.	required
`z_threshold`	`float`	Z-score threshold.	`3.0`
`max_delay`	`str`	Maximum delay for cascade detection.	`'10min'`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	signal_uuid, leader_count, follower_count, leader_ratio, rank