Create an AutoML job for time-series forecasting using the API - Amazon SageMaker

Create an AutoML job for time-series forecasting using the API

Forecasting in machine learning refers to the process of predicting future outcomes or trends based on historical data and patterns. By analyzing past time-series data and identifying underlying patterns, machine learning algorithms can make predictions and provide valuable insights into future behavior. In forecasting, the goal is to develop models that can accurately capture the relationship between input variables and the target variable over time. This involves examining various factors such as trends, seasonality, and other relevant patterns within the data. The collected information is then used to train a machine learning model. The trained model is capable of generating predictions by taking new input data and applying the learned patterns and relationships. It can provide forecasts for a wide range of use cases, such as sales projections, stock market trends, weather forecasts, demand forecasting, and many more.

The following instructions show how to create an Amazon SageMaker Autopilot job as a pilot experiment for time-series forecasting problem types using SageMaker API Reference.

Note

Tasks such as text and image classification, time-series forecasting, and fine-tuning of large language models are exclusively available through the version 2 of the AutoML REST API. If your language of choice is Python, you can refer to AWS SDK for Python (Boto3) or the AutoMLV2 object of the Amazon SageMaker Python SDK directly.

Users who prefer the convenience of a user interface can use Amazon SageMaker Canvas to access pre-trained models and generative AI foundation models, or create custom models tailored for specific text, image classification, forecasting needs, or generative AI.

You can create an Autopilot time-series forecasting experiment programmatically by calling the CreateAutoMLJobV2 API in any language supported by Amazon SageMaker Autopilot or the AWS CLI.

For information on how this API action translates into a function in the language of your choice, see the See Also section of CreateAutoMLJobV2 and choose an SDK. As an example, for Python users, see the full request syntax of create_auto_ml_job_v2 in AWS SDK for Python (Boto3).

Autopilot trains several model candidates with your target time-series, then selects an optimal forecasting model for a given objective metric. When your model candidates have been trained, you can find the best candidate metrics in the response to DescribeAutoMLJobV2 at BestCandidate.

The following sections define the mandatory and optional input request parameters for the CreateAutoMLJobV2 API used in time-series forecasting.

Note

Refer to the notebook Time-Series Forecasting with Amazon SageMaker Autopilot for a practical, hands-on time-series forecasting example. In this notebook, you use Amazon SageMaker Autopilot to train a time-series model and produce predictions using the trained model. The notebook provides instructions for retrieving a ready-made dataset of tabular historical data on Amazon S3.

Prerequisites

Before using Autopilot to create a time-series forecasting experiment in SageMaker, make sure to:

  • Prepare your time-series dataset. Dataset preparation involves collecting relevant data from various sources, cleaning and filtering it to remove noise and inconsistencies, and organizing it into a structured format. See Time-series datasets format and missing values filling methods to learn more about time-series formats requirements in Autopilot. Optionally, you can supplement your dataset with the public holiday calendar of the country of your choice to capture associated patterns. For more information on holiday calendars, see National holiday calendars.

    Note

    We recommend providing at least 3-5 historical data points for each 1 future data point you want to predict. For example, to forecast 7 days ahead (horizon of 1 week) based on daily data, train your model on a minimum of 21-35 days of historical data. Make sure to provide enough data to capture seasonal and recurrent patterns.

  • Place your time-series data in an Amazon S3 bucket.

  • Grant full access to the Amazon S3 bucket containing your input data for the SageMaker execution role used to run your experiment. Once this is done, you can use the ARN of this execution role in Autopilot API requests.

    • For information on retrieving your SageMaker execution role, see Get execution role.

    • For information on granting your SageMaker execution role permissions to access one or more specific buckets in Amazon S3, see Add Additional Amazon S3 Permissions to a SageMaker Execution Role in Create execution role.

Required parameters

When calling CreateAutoMLJobV2 to create an Autopilot experiment for time-series forecasting, you must provide the following values:

  • An AutoMLJobName to specify the name of your job. The name should be of type string, and should have a minimum length of 1 character and a maximum length of 32.

  • At least one AutoMLJobChannel in AutoMLJobInputDataConfig in which you specify the name of the Amazon S3 bucket that contains your data. Optionally, you can specify the content (CSV or Parquet files) and compression (GZip) types.

  • An AutoMLProblemTypeConfig of type TimeSeriesForecastingJobConfig to configure the settings of your time-series forecasting job. In particular, you must specify:

    • The frequency of predictions, which refers to the desired granularity (hourly, daily, monthly, etc) of your forecast.

      Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

      The valid values for each frequency are the following:

      • Minute - 1-59

      • Hour - 1-23

      • Day - 1-6

      • Week - 1-4

      • Month - 1-11

      • Year - 1

    • The horizon of predictions in your forecast, which refers to the number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

    • A TimeSeriesConfig in which you define the schema of your dataset to map the column headers to your forecast by specifying:

      • A TargetAttributeName: The column that contains historical data of the target field to forecast.

      • A TimestampAttributeName: The column that contains a point in time at which the target value of a given item is recorded.

      • A ItemIdentifierAttributeName: The column that contains the item identifiers for which you want to predict the target value.

    The following is an example of those request parameters. In this example, you are setting up a daily forecast for the expected quantity or level of demand of specific items over a period of 20 days.

    "AutoMLProblemTypeConfig": { "ForecastFrequency": "D", "ForecastHorizon": 20, "TimeSeriesConfig": { "TargetAttributeName": "demand", "TimestampAttributeName": "timestamp", "ItemIdentifierAttributeName": "item_id" },
  • An OutputDataConfig to specify the Amazon S3 output path to store the artifacts of your AutoML job.

  • A RoleArn to specify the ARN of the role used to access your data. You can use the ARN of the execution role to which you have granted access to your data.

All other parameters are optional. For example, you can set specific forecast quantiles, choose a filling method for missing values in the dataset, or define how to aggregate data that does not align with forecast frequency. To learn how to set those additional parameters, see Optional parameters.

Optional parameters

The following sections provide details of some optional parameters that you can pass to your time-series forecasting AutoML job.

By default, your Autopilot job trains a pre-defined list of algorithms on your dataset. However, you can provide a subset of the default selection of algorithms.

For time-series forecasting, you must choose TimeSeriesForecastingJobConfig as the type of AutoMLProblemTypeConfig.

Then, you can specify an array of selected AutoMLAlgorithms in the AlgorithmsConfig attribute of CandidateGenerationConfig.

The following is an example of an AlgorithmsConfig attribute listing exactly three algorithms ("cnn-qr", "prophet", "arima") in its AutoMLAlgorithms field.

{ "AutoMLProblemTypeConfig": { "TimeSeriesForecastingJobConfig": { "CandidateGenerationConfig": { "AlgorithmsConfig":[ {"AutoMLAlgorithms":["cnn-qr", "prophet", "arima"]} ] }, }, }, }

For the list of available algorithms for time-series forecasting, see AutoMLAlgorithms. For details on each algorithm, see Algorithms support for time-series forecasting.

Autopilot trains 6 models candidates with your target time-series, then combines these models using a stacking ensemble method to create an optimal forecasting model for a given objective metric. Each Autopilot forecasting model generates a probabilistic forecast by producing forecasts at quantiles between P1 and P99. These quantiles are used to account for forecast uncertainty. By default, forecasts will be generated for the 0.1 (p10), 0.5 (p50), and 0.9 (p90). You can choose to specify your own quantiles.

In Autopilot, you can specify up to five forecast quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher in the ForecastQuantiles attribute of TimeSeriesForecastingJobConfig.

In the following example, you are setting up a daily 10th, 25th, 50th, 75th, and 90th percentile forecast for the expected quantity or level of demand of specific items over a period of 20 days.

"AutoMLProblemTypeConfig": { "ForecastFrequency": "D", "ForecastHorizon": 20, "ForecastQuantiles": ["p10", "p25", "p50", "p75", "p90"], "TimeSeriesConfig": { "TargetAttributeName": "demand", "TimestampAttributeName": "timestamp", "ItemIdentifierAttributeName": "item_id" },

To create a forecast model (also referred to as the best model candidate from your experiment), you must specify a forecast frequency. The forecast frequency determines the frequency of predictions in your forecasts. For example, monthly sales forecasts. Autopilot best model can generate forecasts for data frequencies that are higher than the frequency at which your data is recorded.

During training, Autopilot aggregates any data that does not align with the forecast frequency you specify. For example, you might have some daily data but specify a weekly forecast frequency. Autopilot aligns the daily data based on the week that it belongs in. Autopilot then combines it into single record for each week.

During aggregation, the default transformation method is to sum the data. You can configure the aggregation when you create your AutoML job in the Transformations attribute of TimeSeriesForecastingJobConfig. The supported aggregation methods are sum (default), avg, first, min, max. Aggregation is only supported for the target column.

In the following example, you configure the aggregation to calculate the average of the individual promo forecasts to provide the final aggregated forecast values.

"Transformations": { "Aggregation": { "promo": "avg" } }

Autopilot provides a number of filling methods to handle missing values in the target and other numeric columns of your time-series datasets. For information on the list of supported filling methods and their available filling logic, see Handle missing values.

You configure your filling strategy in the Transformations attribute of TimeSeriesForecastingJobConfig when creating your AutoML job.

To set a filling method, you need to provide a key-value pair:

  • The key is the name of the column for which you want to specify the filling method.

  • The value associated with the key is an object that defines the filling strategy for that column.

You can specify multiple filling methods for a single column.

To set a specific value for the filling method, you should set the fill parameter to the desired filling method value (for example "backfill" : "value"), and define the actual filling value in an additional parameter suffixed with "_value". For example, to set backfill to a value of 2, you must include two parameters: "backfill": "value" and "backfill_value":"2".

In the following example, you specify the filling strategy for the incomplete data column, "price" as follows: All missing values between the first data point of an item and the last are set to 0 after which all missing values are filled with the value 2 until the end date of the dataset.

"Transformations": { "Filling": { "price": { "middlefill" : "zero", "backfill" : "value", "backfill_value": "2" } } }

Autopilot produces accuracy metrics to evaluate the model candidates and help you choose which to use to generate forecasts. When you run a time-series forecasting experiment, you can either choose AutoML to let Autopilot optimize the predictor for you, or you can manually choose an algorithm for your predictor.

By default, Autopilot uses the Average Weighted Quantile Loss. However, you can configure the objective metric when you create your AutoML job in the MetricName attribute of AutoMLJobObjective.

For the list of available algorithms, see Algorithms support for time-series forecasting.

In Autopilot, you can incorporate a feature-engineered dataset of national holiday information to your time-series. Autopilot provide native support for the holiday calendars of over 250 countries. After you choose a country, Autopilot applies that country’s holiday calendar to every item in your dataset during training. This allows the model to identify patterns associated with specific holidays.

You can enable the holiday featurization when you create your AutoML job by passing an HolidayConfigAttributes object to the HolidayConfig attribute of TimeSeriesForecastingJobConfig. The HolidayConfigAttributes object contains the two letters CountryCode attribute that determines the country of the public national holiday calendar used to augment your time-series dataset.

Refer to Country Codes for the list of supported calendars and their corresponding country code.

Autopilot allows you to automatically deploy your forecast model to an endpoint. To enable automatic deployment for the best model candidate of an AutoML job, include a ModelDeployConfig in the AutoML job request. This allows the deployment of the best model to a SageMaker endpoint. Below are the available configurations for customization.