# SsaForecastingEstimator Class

## Definition

Forecasts using Singular Spectrum Analysis.

public sealed class SsaForecastingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.TimeSeries.SsaForecastingTransformer>
type SsaForecastingEstimator = class
interface IEstimator<SsaForecastingTransformer>
Public NotInheritable Class SsaForecastingEstimator
Implements IEstimator(Of SsaForecastingTransformer)
Inheritance
SsaForecastingEstimator
Implements

## Remarks

To create this estimator, use ForecastBySsa

### Input and Output Columns

There is only one input column. The input column must be Single where a Single value indicates a value at a timestamp in the time series.

It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

### Estimator Characteristics

Does this estimator need to look at the data to train its parameters? Yes
Input column data type Single
Output column data type Vector of Single
Exportable to ONNX No

### Estimator Characteristics

Is normalization required? No
Is caching required? No
Required NuGet in addition to Microsoft.ML Microsoft.ML.TimeSeries

### Training Algorithm Details

This class implements the general anomaly detection transform based on Singular Spectrum Analysis (SSA). SSA is a powerful framework for decomposing the time-series into trend, seasonality and noise components as well as forecasting the future values of the time-series. In principle, SSA performs spectral analysis on the input time-series where each component in the spectrum corresponds to a trend, seasonal or noise component in the time-series. For details of the Singular Spectrum Analysis (SSA), refer to this document.