TimeSeriesForecasterBatches

struct · iOS 18.0+ · SDK 27.1

struct TimeSeriesForecasterBatches<Scalar> where Scalar : MLShapedArrayScalar

A sequence of forecaster batches on a time series shaped array. A time-series forecaster takes a series of samples and produces a prediction of the next samples. For example the sequence `[1, 2, 3, 4]` could predict `[5, 6]`. To train a forecaster, each training batch contains the input samples along with the annotations (ground truth predictions). For example a batch could have this: ``` features = [ [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], ] annotations = [ [5, 6], [6, 7], [7, 8], ] ``` The shape of the features in the sequence is `[batchSize, inputWindowSize, featureSize]` and the shape of the annotations is `[batchSize, forecastWindowSize, annotationSize]`. The batch sequence will return as many feature-annotation examples as fit in the input. For example, an input sequence size of 10 with an input sample count of 4 and a prediction sample count of 2 will produce 5 examples: ``` features: [1, 2, 3, 4], annotations: [5, 6] features: [2, 3, 4, 5], annotations: [6, 7] features: [3, 4, 5, 6], annotations: [7, 8] features: [4, 5, 6, 7], annotations: [8, 9] features: [5, 6, 7, 8], annotations: [9, 10] ``` Note that 9 and 10 are never used as features because there would be no annotations for those examples.

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