# update(_:with:) Updates a model with a new batch of examples. ``` func update(_ model: inout LinearTimeSeriesForecaster.Transformer, with input: AnnotatedBatch) async throws -> Scalar ``` ## Parameters `model` The model to update. `input` A shaped array of windowed features. The shape should be `[batchSize, inputWindowSize, featureSize]`. ## Discussion Use [`TimeSeriesForecasterBatches`](/documentation/CreateMLComponents/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(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)") } } ``` --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)