---
type: concept
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  pageDirectory: concepts
  sources:
    - data-preparation-for-forecasting-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
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  aliases:
    - configurable-forecasting-data-settings-in-automl
    - CFDSIA
  citations:
    - file: data-preparation-for-forecasting-databricks-on-aws.md
title: Configurable Forecasting Data Settings in AutoML
description: The ability for users to adjust data preparation options (imputation, aggregation) during AutoML experiment setup via UI dropdowns or the Python API.
tags:
  - databricks
  - automl
  - forecasting
  - configuration
timestamp: "2026-06-19T09:43:27.108Z"
---

---
title: Configurable Forecasting Data Settings in AutoML
summary: Adjustable data preparation options in AutoML for forecasting tasks, including imputation methods, time series aggregation, and train/validation/test splitting behavior.
sources:
  - data-preparation-for-forecasting-databricks-on-aws.md
kind: concept
tags:
  - automl
  - forecasting
  - data-preparation
  - databricks
aliases:
  - configurable-forecasting-data-settings-in-automl
  - CFDSIA
confidence: 0.95
provenanceState: extracted
inferredParagraphs: 1
---

# Configurable Forecasting Data Settings in AutoML

**Configurable Forecasting Data Settings in AutoML** refers to the adjustable data preparation options available when training forecasting models using AutoML on Databricks. These settings control how AutoML handles missing values, aggregates time series data, and splits data for evaluation. You can configure these options during experiment setup in the AutoML UI. ^[data-preparation-for-forecasting-databricks-on-aws.md]

## Supported Data Feature Types

AutoML supports the following feature types for forecasting. Unsupported types (such as images) are excluded from training:

- Numeric (`ByteType`, `ShortType`, `IntegerType`, `LongType`, `FloatType`, `DoubleType`)
- Boolean
- String (categorical or English text)
- Timestamps (`TimestampType`, `DateType`)
- `ArrayType[Numeric]` (Databricks Runtime 10.4 LTS ML and above)
- `DecimalType` (Databricks Runtime 11.3 LTS ML and above)

^[data-preparation-for-forecasting-databricks-on-aws.md]

## Impute Missing Values

Starting in Databricks Runtime 10.4 LTS ML, you can specify how null values are imputed. In the UI, select a method from the **Impute with** drop-down in the table schema. In the API, use the `imputers` parameter (see the [AutoML Python API reference](/concepts/automl-python-api.md)). ^[data-preparation-for-forecasting-databricks-on-aws.md]

By default, AutoML automatically selects an imputation method based on the column type and content. ^[data-preparation-for-forecasting-databricks-on-aws.md]

If you specify a non-default imputation method, AutoML does **not** perform [Semantic Type Detection](/concepts/semantic-type-detection.md). ^[data-preparation-for-forecasting-databricks-on-aws.md]

## Split Forecasting Data into Train, Validation, and Test Sets

For forecasting tasks, AutoML uses **time series cross-validation** rather than random splitting. This method incrementally extends the training dataset chronologically and performs validation on subsequent time points. Cross-validation ensures that the forecasting model is rigorously tested against unseen future data, maintaining prediction relevance and accuracy. ^[data-preparation-for-forecasting-databricks-on-aws.md]

The number of cross-validation folds depends on input table characteristics such as the number of time series, the presence of covariates, and the time series length. ^[data-preparation-for-forecasting-databricks-on-aws.md]

## Time Series Aggregation

When a time series has multiple values at the same timestamp, AutoML uses the **average** of those values by default. ^[data-preparation-for-forecasting-databricks-on-aws.md]

To use the **sum** instead, edit the source code notebook generated by the trial runs. In the **Aggregate data by …** cell, change `.agg(y=(target_col, "avg"))` to `.agg(y=(target_col, "sum"))`, as shown:

```python
group_cols = [time_col] + id_cols

df_aggregation = df_loaded \
  .groupby(group_cols) \
  .agg(y=(target_col, "sum")) \
  .reset_index() \
  .rename(columns={ time_col : "ds" })
```

^[data-preparation-for-forecasting-databricks-on-aws.md]

## Related Concepts

- [AutoML Experiment Setup](/concepts/automl-classification-experiment-setup.md) – Configuring forecasting experiments in the UI.
- [AutoML Python API Reference](/concepts/automl-python-api.md) – Configuring these settings programmatically.
- [Time Series Cross-Validation](/concepts/time-series-cross-validation.md) – The chronological splitting method used for forecasting.
- Notebook Generation in AutoML – Modifying generated source notebooks for custom behavior.
- [Semantic Type Detection](/concepts/semantic-type-detection.md) – AutoML’s automatic data type inference.

## Sources

- data-preparation-for-forecasting-databricks-on-aws.md

# Citations

1. [data-preparation-for-forecasting-databricks-on-aws.md](/references/data-preparation-for-forecasting-databricks-on-aws-d40acbea.md)
