# TimeSeriesPredictionEngine<TSrc,TDst> Class

## Definition

A class that runs the previously trained model (and the preceding transform pipeline) on the in-memory data, one example at a time. This can also be used with trained pipelines that do not end with a predictor: in this case, the 'prediction' will be just the outcome of all the transformations.

public sealed class TimeSeriesPredictionEngine<TSrc,TDst> : Microsoft.ML.PredictionEngineBase<TSrc,TDst> where TSrc : class where TDst : class, new()
type TimeSeriesPredictionEngine<'Src, 'Dst (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))> = class
inherit PredictionEngineBase<'Src, 'Dst (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))>
Public NotInheritable Class TimeSeriesPredictionEngine(Of TSrc, TDst)
Inherits PredictionEngineBase(Of TSrc, TDst)

#### Type Parameters

TSrc

The user-defined type that holds the example.

TDst

The user-defined type that holds the prediction.

Inheritance
TimeSeriesPredictionEngine<TSrc,TDst>

## Constructors

 Contructor for creating time series specific prediction engine. It allows the time series model to be updated with the observations seen at prediction time via CheckPoint(IHostEnvironment, String)

## Properties

 Provides output schema. (Inherited from PredictionEngineBase)

## Methods

 Checkpoints TimeSeriesPredictionEngine to a Stream with the updated state. Checkpoints TimeSeriesPredictionEngine to disk with the updated state. (Inherited from PredictionEngineBase) Forecasting only task. Run prediction pipeline on one example. (Inherited from PredictionEngineBase) Performs prediction. In the case of forecasting only task example can be left as null. If example is not null then it could be used to update forecasting models with new obervation. Performs prediction. In the case of forecasting only task example can be left as null. If example is not null then it could be used to update forecasting models with new obervation. For anomaly detection the model is always updated with example. Performs prediction. In the case of forecasting only task example can be left as null. If example is not null then it could be used to update forecasting models with new obervation. For anomaly detection the model is always updated with example.