TimeSeriesCatalog Class

Create SrCnnAnomalyEstimator, which detects timeseries anomalies using SRCNN algorithm.

Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).

Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).

Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm.

Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm.

Create IidChangePointEstimator, which predicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

Create IidChangePointEstimator, which predicts change points in an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

In time series data, seasonality (or periodicity) is the presence of variations that occur at specific regular intervals, such as weekly, monthly, or quarterly.

This method detects this predictable interval (or period) by adopting techniques of fourier analysis. Assuming the input values have the same time interval (e.g., sensor data collected at every second ordered by timestamps), this method takes a list of time-series data, and returns the regular period for the input seasonal data, if a predictable fluctuation or pattern can be found that recurs or repeats over this period throughout the input values.

Returns -1 if no such pattern is found, that is, the input values do not follow a seasonal fluctuation.

Create SsaSpikeEstimator, which predicts spikes in time series using Singular Spectrum Analysis (SSA).

Create SsaSpikeEstimator, which predicts spikes in time series using Singular Spectrum Analysis (SSA).

Singular Spectrum Analysis (SSA) model for univariate time-series forecasting. For the details of the model, refer to http://arxiv.org/pdf/1206.6910.pdf.

Create RootCause, which localizes root causes using decision tree algorithm.

Outputs an ordered list of RootCauses. The order corresponds to which prepared cause is most likely to be the root cause.