---
title: "update(_:with:)"
framework: createmlcomponents
role: symbol
role_heading: Instance Method
path: "createmlcomponents/lineartimeseriesforecaster/update(_:with:)"
---

# update(_:with:)

Updates a model with a new batch of examples.

## Declaration

```swift
func update(_ model: inout LinearTimeSeriesForecaster<Scalar>.Transformer, with input: AnnotatedBatch<Scalar>) async throws -> Scalar
```

## Parameters

- `model`: The model to update.
- `input`: A shaped array of windowed features. The shape should be [batchSize, inputWindowSize, featureSize].

## Discussion

Discussion Use TimeSeriesForecasterBatches to convert a shaped array of features into batches of windowed features and annotations. Here is an example of training a forecaster: let estimator = LinearTimeSeriesForecaster<Float>(configuration: configuration) var model = estimator.makeTransformer()

let batches = try TimeSeriesForecasterBatches(     features: features,       // shape [N, featureSize]     annotations: annotations, // shape [N, annotationSize]     batchSize: 32,     inputWindowSize: configuration.inputWindowSize,     forecastWindowSize: configuration.forecastWindowSize,     shufflesBatches: true )

for iteration in 0 ..< configuration.maximumIterationCount {     for batch in batches {         let loss = try await estimator.update(&model, with: batch)         print("Loss: \(loss)")     } }

## See Also

### Updating and fitting

- [update(_:withWindows:eventHandler:)](createmlcomponents/lineartimeseriesforecaster/update(_:withwindows:eventhandler:).md)
- [fitted(to:eventHandler:)](createmlcomponents/lineartimeseriesforecaster/fitted(to:eventhandler:).md)
- [fitted(to:validateOn:eventHandler:)](createmlcomponents/lineartimeseriesforecaster/fitted(to:validateon:eventhandler:).md)
- [fitted(toWindows:eventHandler:)](createmlcomponents/lineartimeseriesforecaster/fitted(towindows:eventhandler:).md)
- [fitted(toWindows:validateOn:eventHandler:)](createmlcomponents/lineartimeseriesforecaster/fitted(towindows:validateon:eventhandler:).md)