################### ### Main Config ### ################### # Specifies the detection window start time in timeseries. # If you want to use the following MAX_ANOMALY_TIME_AGO # make this parameter '0' DETECTION_WINDOW_START_TIME 0 # Only show anomalies no older than this. # If this is set to 0, then only output an anomaly # if it occurs on the last time-stamp. MAX_ANOMALY_TIME_AGO 999999999 # Denotes how much should the time-series be aggregated by. # If set to 1 or less, this setting is ignored. AGGREGATION 1 # OP_TYPE specifies the operation type. # Options: DETECT_ANOMALY, # UPDATE_MODEL, # TRANSFORM_INPUT OP_TYPE DETECT_ANOMALY # TS_MODEL specifies the time-series # model type. # Options: AutoForecastModel # DoubleExponentialSmoothingModel # MovingAverageModel # MultipleLinearRegressionModel # NaiveForecastingModel # OlympicModel # PolynomialRegressionModel # RegressionModel # SimpleExponentialSmoothingModel # TripleExponentialSmoothingModel # WeightedMovingAverageModel # SpectralSmoother # NullModel TS_MODEL OlympicModel # AD_MODEL specifies the anomaly-detection # model type. # Options: ExtremeLowDensityModel # AdaptiveKernelDensityChangePointDetector # KSigmaModel # NaiveModel # DBScanModel # SimpleThresholdModel AD_MODEL ExtremeLowDensityModel # Type of the simple threshold model. # Options: AdaptiveMaxMinSigmaSensitivity # AdaptiveKSigmaSensitivity # SIMPLE_THRESHOLD_TYPE # Specifies the input src. # Options: STDIN # CSV INPUT CSV # Specifies the output src. # Options: STD_OUT, # ANOMALY_DB # GUI # PLOT OUTPUT STD_OUT # THRESHOLD specifies the threshold (e.g., sensitivity) for anomaly detection model. # Comment out to auto-detect all thresholds. # Options: mapee,mae,smape,mape,mase, # or single numeric for simple threshold model. # THRESHOLD mape#10,mase#15 ##################################### ### Olympic Forecast Model Config ### ##################################### # The possible time-shifts for Olympic Scoring. TIME_SHIFTS 0,1 # The possible base windows for Olympic Scoring. BASE_WINDOWS 24,168 # Period specifies the periodicity of the # time-series (e.g., the difference between successive time-stamps). # Options: (numeric) # 0 - auto detect. # -1 - disable. PERIOD -1 # Fill missing values. # Options: 0,1 FILL_MISSING 0 # NUM_WEEKS specifies the number of weeks # to use in OlympicScoring. NUM_WEEKS 8 # NUM_TO_DROP specifies the number of # highest and lowest points to drop. NUM_TO_DROP 0 # If dynamic parameters is set to 1, then # EGADS will dynamically vary parameters (NUM_WEEKS) # to produce the best fit. DYNAMIC_PARAMETERS 0 ############################ ### NaiveModel Config ### ############################ # Window size where the spike is to be found. WINDOW_SIZE 0.1 ################################################### ### ExtremeLowDensityModel & DBScanModel Config ### ################################################### # Denotes the expected % of anomalies # in your data. AUTO_SENSITIVITY_ANOMALY_PCNT 0.01 # Refers to the cluster standard deviation. AUTO_SENSITIVITY_SD 3.0 ####################################################### ### AdaptiveKernelDensityChangePointDetector Config ### ####################################################### # Change point detection parameters PRE_WINDOW_SIZE 48 POST_WINDOW_SIZE 48 CONFIDENCE 0.8 ############################### ### SpectralSmoother Config ### ############################### # WINDOW_SIZE should be greater than the size of longest important seasonality. # By default it is set to 192 = 8 * 24 which is worth of 8 days (> 1 week) for hourly time-series. WINDOW_SIZE 192 # FILTERING_METHOD specifies the filtering method for Spectral Smoothing # Options: GAP_RATIO (Recommended: FILTERING_PARAM = 0.01) # EIGEN_RATIO (Recommended: FILTERING_PARAM = 0.1) # EXPLICIT (Recommended: FILTERING_PARAM = 10) # K_GAP (Recommended: FILTERING_PARAM = 8) # VARIANCE (Recommended: FILTERING_PARAM = 0.99) # SMOOTHNESS (Recommended: FILTERING_PARAM = 0.97) FILTERING_METHOD GAP_RATIO FILTERING_PARAM 0.01