merlion.models.anomaly.change_point package
Contains all change point detection algorithms. These models implement the anomaly detector interface, but they are specialized for detecting change points in time series.
Bayesian online change point detection algorithm. |
Submodules
merlion.models.anomaly.change_point.bocpd module
Bayesian online change point detection algorithm.
- class merlion.models.anomaly.change_point.bocpd.ChangeKind(value)
Bases:
Enum
Enum representing the kinds of changes points we would like to detect. Enum values correspond to the Bayesian
ConjPrior
class used to detect each sort of change point.- Auto = None
Automatically choose the Bayesian conjugate prior we would like to use.
- LevelShift = <class 'merlion.utils.conj_priors.MVNormInvWishart'>
Model data points with a normal distribution, to detect level shifts.
- TrendChange = <class 'merlion.utils.conj_priors.BayesianMVLinReg'>
Model data points as a linear function of time, to detect trend changes.
- class merlion.models.anomaly.change_point.bocpd.BOCPDConfig(change_kind=ChangeKind.Auto, cp_prior=0.01, lag=None, min_likelihood=1e-12, max_forecast_steps=None, target_seq_index=None, transform=None, enable_calibrator=False, max_score=1000, threshold=None, enable_threshold=True, normalize=None, **kwargs)
Bases:
ForecasterConfig
,NoCalibrationDetectorConfig
Config class for
BOCPD
(Bayesian Online Change Point Detection).Base class of the object used to configure an anomaly detection model.
- Parameters
change_kind (
Union
[str
,ChangeKind
]) – the kind of change points we would like to detectcp_prior – prior belief probability of how frequently changepoints occur
lag – the maximum amount of delay/lookback (in number of steps) allowed for detecting change points. If
lag
isNone
, we will consider the entire history. Note: we do not recommendlag = 0
.min_likelihood – we will discard any hypotheses whose probability of being a change point is lower than this threshold. Lower values improve accuracy at the cost of time and space complexity.
max_forecast_steps – the maximum number of steps the model is allowed to forecast. Ignored.
target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.
transform – Transformation to pre-process input time series.
enable_calibrator –
False
because this config assumes calibrated outputs from the model.max_score – maximum possible uncalibrated anomaly score
threshold – the rule to use for thresholding anomaly scores
enable_threshold – whether to enable the thresholding rule when post-processing anomaly scores
normalize – Pre-trained normalization transformation (optional).
- to_dict(_skipped_keys=None)
- Returns
dict with keyword arguments used to initialize the config class.
- property change_kind: ChangeKind
- Return type
- class merlion.models.anomaly.change_point.bocpd.BOCPD(config=None)
Bases:
ForecastingDetectorBase
Bayesian online change point detection algorithm described by Adams & MacKay (2007). At a high level, this algorithm models the observed data using Bayesian conjugate priors. If an observed value deviates too much from the current posterior distribution, it is likely a change point, and we should start modeling the time series from that point forwards with a freshly initialized Bayesian conjugate prior.
The
get_anomaly_score()
method returns a z-score corresponding to the probability of each point being a change point. Theforecast()
method returns the predicted values (and standard error) of the underlying piecewise model on the relevant data.- Parameters
config (
Optional
[BOCPDConfig
]) – model configuration
- config_class
alias of
BOCPDConfig
- property last_train_time
- Returns
the last time (as a
pandas.Timestamp
) that the model was trained on
- property n_seen
- Returns
the number of data points seen so far
- property change_kind: ChangeKind
- Return type
- Returns
the kind of change points we would like to detect
- property cp_prior: float
- Return type
float
- Returns
prior belief probability of how frequently changepoints occur
- property lag: int
- Return type
int
- Returns
the maximum amount of delay allowed for detecting change points. A higher lag can increase recall, but it may decrease precision.
- property min_likelihood: float
- Return type
float
- Returns
we will not consider any hypotheses (about whether a particular point is a change point) with likelihood lower than this threshold
- train_pre_process(train_data, require_even_sampling, require_univariate)
Applies pre-processing steps common for training most models.
- Parameters
train_data (
TimeSeries
) – the original time series of training datarequire_even_sampling (
bool
) – whether the model assumes that training data is sampled at a fixed frequencyrequire_univariate (
bool
) – whether the model only works with univariate time series
- Return type
- Returns
the training data, after any necessary pre-processing has been applied
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr (
bool
) – whether to return the inter-quartile range for the forecast. Note that not all models support this option.return_prev (
bool
) – whether to return the forecast fortime_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Union
[Tuple
[TimeSeries
,TimeSeries
],Tuple
[TimeSeries
,TimeSeries
,TimeSeries
]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- get_figure(*, time_series=None, time_stamps=None, time_series_prev=None, plot_anomaly=True, filter_scores=True, plot_forecast=False, plot_forecast_uncertainty=False, plot_time_series_prev=False)
- Parameters
time_series (
Optional
[TimeSeries
]) – the time series over whose timestamps we wish to make a forecast. Exactly one oftime_series
ortime_stamps
should be provided.time_stamps (
Optional
[List
[int
]]) – a list of timestamps we wish to forecast for. Exactly one oftime_series
ortime_stamps
should be provided.time_series_prev (
Optional
[TimeSeries
]) – aTimeSeries
immediately precedingtime_stamps
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_stamps
immediately follows the training data.plot_anomaly – Whether to plot the model’s predicted anomaly scores.
filter_scores – whether to filter the anomaly scores by the post-rule before plotting them.
plot_forecast – Whether to plot the model’s forecasted values.
plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast values. Not supported for all models.
plot_time_series_prev – whether to plot
time_series_prev
(and the model’s fit for it). Only used iftime_series_prev
is given.
- Return type
- Returns
a
Figure
of the model’s anomaly score predictions and/or forecast.
- update(time_series)
Updates the BOCPD model’s internal state using the time series values provided.
- Parameters
time_series (
TimeSeries
) – time series whose values we are using to update the internal state of the model- Returns
anomaly score associated with each point (based on the probability of it being a change point)
- train(train_data, anomaly_labels=None, train_config=None, post_rule_train_config=None)
Trains the underlying forecaster (unsupervised) on the training data. Converts the forecast into anomaly scores, and and then trains the post-rule for filtering anomaly scores (supervised, if labels are given) on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.anomaly_labels (
Optional
[TimeSeries
]) – aTimeSeries
indicating which timestamps are anomalous. Optional.train_config – Additional training configs, if needed. Only required for some models.
post_rule_train_config – The config to use for training the model’s post-rule. The model’s default post-rule train config is used if none is supplied here.
- Return type
- Returns
A
TimeSeries
of the model’s anomaly scores on the training data.
- get_anomaly_score(time_series, time_series_prev=None)
Returns the model’s predicted sequence of anomaly scores.
- Parameters
time_series (
TimeSeries
) – theTimeSeries
we wish to predict anomaly scores for.time_series_prev (
Optional
[TimeSeries
]) – aTimeSeries
immediately precedingtime_series
. If given, we use it to initialize the time series anomaly detection model. Otherwise, we assume thattime_series
immediately follows the training data.
- Return type
- Returns
a univariate
TimeSeries
of anomaly scores