Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. You collect time series data and Luminol can:

• Given a time series, detect if the data contains any anomaly and gives you back a time window where the anomaly happened in, a time stamp where the anomaly reaches its severity, and a score indicating how severe is the anomaly compare to others in the time series.
• Given two time series, help find their correlation coefficient. Since the correlation mechanism allows a shift room, you are able to correlate two peaks that are slightly apart in time.

Luminol is configurable in a sense that you can choose which specific algorithm you want to use for anomaly detection or correlation. In addition, the library does not rely on any predefined threshold on the values of a time series. Instead, it assigns each data point an anomaly score and identifies anomalies using the scores.

By using the library, we can establish a logic flow for root cause analysis. For example, suppose there is a spike in network latency:

• Anomaly detection discovers the spike in network latency time series
• Get the anomaly period of the spike, and correlate with other system metrics(GC, IO, CPU, etc.) in the same time range
• Get a ranked list of correlated metrics, and the root cause candidates are likely to be on the top.

Investigating the possible ways to automate root cause analysis is one of the main reasons we developed this library and it will be a fundamental part of the future work.

make sure you have python, pip, numpy, and install directly through pip:

pip install luminol

the most up-to-date version of the library is 0.1.

This is a quick start guide for using luminol for time series analysis.

1. import the library

   import luminol

2. conduct anomaly detection on a single time series ts.

   detector = luminol.anomaly_detector.AnomalyDetector(ts)
   anomalies = detector.get_anomalies()

3. if there is anomaly, correlate the first anomaly period with a secondary time series ts2.

   if anomalies:
     time_period = anomalies[0].get_time_window()
     correlator = luminol.correlator.Correlator(ts, ts2, time_period)

4. print the correlation coefficient

   print correlator.get_correlation_result().coefficient

These are really simple use of luminol. For information about the parameter types, return types and optional parameters, please refer to the API.

Modules in Luminol refers to customized classes developed for better data representation, which are Anomaly, CorrelationResult and TimeSeries. ####Anomaly class luminol.modules.anomaly.Anomaly
It contains these attributes:

self.start_timestamp: # epoch seconds represents the start of the anomaly period.
self.end_timestamp: # epoch seconds represents the end of the anomaly period.
self.anomaly_score: # a score indicating how severe is this anomaly.
self.exact_timestamp: # epoch seconds indicates when the anomaly reaches its severity.

It has these public methods:

• get_time_window(): returns a tuple (start_timestamp, end_timestamp).

####CorrelationResult class luminol.modules.correlation_result.CorrelationResult
It contains these attributes:

self.coefficient: # correlation coefficient.
self.shift: # the amount of shift needed to get the above coefficient.
self.shifted_coefficient: # a correlation coefficient with shift taken into account.

####TimeSeries class luminol.modules.time_series.TimeSeries

__init__(self, series)

• series(dict): timestamp -> value

It has a various handy methods for manipulating time series, including generator iterkeys, itervalues, and iteritems. It also supports binary operations such as add and subtract. Please refer to the code and inline comments for more information.

The library contains two classes: AnomalyDetector and Correlator, and there are two sets of APIs, one corresponding to each class. There are also customized modules for better data representation. The Modules section in this documentation may provide useful information as you walk through the APIs. ####AnomalyDetector class luminol.anomaly_detector.AnomalyDetecor

__init__(self, time_series, baseline_time_series=None, score_only=False, score_threshold=None,
score_percentile_threshold=None, algorithm_name=None, algorithm_params=None, 
refine_algorithm_name=None, refine_algorithm_params=None)

• time_series: The metric you want to conduct anomaly detection on. It can have the following three types:

  1. string: # path to a csv file
  2. dict: # timestamp -> value
  3. lumnol.modules.time_series.TimeSeries

• baseline_time_series: an optional baseline time series of one the types mentioned above.

• score only(bool): if asserted, anomaly scores for the time series will be available, while anomaly periods will not be identified.

• score_threshold: if passed, anomaly scores above this value will be identified as anomaly. It can override score_percentile_threshold.

• score_precentile_threshold: if passed, anomaly scores above this percentile will be identified as anomaly. It can not override score_threshold.

• algorithm_name(string): if passed, the specific algorithm will be used to compute anomaly scores.

• algorithm_params(dict): additional parameters for algorithm specified by algorithm_name.

• refine_algorithm_name(string): if passed, the specific algorithm will be used to compute the time stamp of severity within each anomaly period.

• refine_algorithm_params(dict): additional parameters for algorithm specified by refine_algorithm_params.

Available algorithms and their additional parameters are:

1. 'bitmap_detector': # behaves well for huge data sets, and it is the default detector.
   {
      'precision'(4): # how many sections to categorize values,
      'lag_window_size'(2% of the series length): # lagging window size,
      'future_window_size'(2% of the series length): # future window size,
      'chunk_size'(2): # chunk size.
   }
2. 'default_detector': # used when other algorithms fails, not meant to be explicitly used.
3. 'derivative_detector': # meant to be used when abrupt changes of value are of main interest.
   {
      'smoothing factor'(0.2): # smoothing factor used to compute exponential moving averages
                                # of derivatives.
   }
4. 'exp_avg_detector': # meant to be used when values are in a roughly stationary range.
                        # and it is the default refine algorithm.
   {
      'smoothing factor'(0.2): # smoothing factor used to compute exponential moving averages.
      'lag_window_size'(20% of the series length): # lagging window size.
      'use_lag_window'(False): # if asserted, a lagging window of size lag_window_size will be used.
   }

It may seem vague for the meanings of some parameters above. Here are some useful insights:

• Bitmap
• Exponential Moving Avg

The AnomalyDetector class has the following public methods:

• get_all_scores(): returns an anomaly score time series of type TimeSeries.
• get_anomalies(): return a list of Anomaly objects.

####Correlator class luminol.correlator.Correlator

__init__(self, time_series_a, time_series_b, time_period=None, use_anomaly_score=False,
algorithm_name=None, algorithm_params=None)

• time_series_a: a time series, for its type, please refer to time_series for AnomalyDetector above.
• time_series_b: a time series, for its type, please refer to time_series for AnomalyDetector above.
• time_period(tuple): a time period where to correlate the two time series.
• use_anomaly_score(bool): if asserted, the anomaly scores of the time series will be used to compute correlation coefficient instead of the original data in the time series.
• algorithm_name: if passed, the specific algorithm will be used to calculate correlation coefficient.
• algorithm_params: any additional parameters for the algorithm specified by algorithm_name.

Available algorithms and their additional parameters are:

1. 'cross_correlator': # when correlate two time series, it tries to shift the series around so that it
                        # can catch spikes that are slightly apart in time.
   {
      'max_shift_seconds'(60): # maximal allowed shift room in seconds,
      'shift_impact'(0.05): # weight of shift in the shifted coefficient.
   }

The Correlator class has the following public methods:

• get_correlation_result(): return a CorrelationResult object.
• is_correlated(threshold=0.7): if coefficient above the passed in threshold, return a CorrelationResult object. Otherwise, return false.

1. Put anomaly scores in a list.

   from luminol.anomaly_detector import AnomalyDetector
   my_detector = AnomalyDetector(ts)
   score = my_detector.get_all_scores()
   anom_score = list()
   for (timestamp, value) in score.iteritems():
     t_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))
     anom_score.append([t_str, value])

2. Correlate with ts2 on every anomaly.

   from luminol.anomaly_detector import AnomalyDetector
   from luminol.correlator import Correlator
   
   my_detector = detector.AnomalyDetector(ts)
   anomalies = my_detector.get_anomalies()
   for a in anomalies:
     time_period = a.get_time_window()
     my_correlator = Correlator(ts, ts2, time_period)
     if my_correlator.is_correlated(threshold=0.8):
       print "ts2 correlate with ts at time period (%d, %d)" % time_period