View Source GoogleApi.AIPlatform.V1.Model.GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig (google_api_ai_platform v0.13.0)

Config that contains the strategy used to generate sliding windows in time series training. A window is a series of rows that comprise the context up to the time of prediction, and the horizon following. The corresponding row for each window marks the start of the forecast horizon. Each window is used as an input example for training/evaluation.

Attributes

column (type: String.t, default: nil) - Name of the column that should be used to generate sliding windows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window with the horizon starting at that row. The column will not be used as a feature in training.
maxCount (type: String.t, default: nil) - Maximum number of windows that should be generated across all time series.
strideLength (type: String.t, default: nil) - Stride length used to generate input examples. Within one time series, every {$STRIDE_LENGTH} rows will be used to generate a sliding window.

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

t()

@type t() ::
  %GoogleApi.AIPlatform.V1.Model.GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig{
    column: String.t() | nil,
    maxCount: String.t() | nil,
    strideLength: String.t() | nil
  }

Functions

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.