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https://issues.apache.org/jira/browse/SPARK-53803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anand updated SPARK-53803:
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External issue ID: https://github.com/apache/spark/pull/52519
External issue URL: https://github.com/apache/spark/pull/52519
> Add ArimaRegression for time series forecasting in MLlib
> --------------------------------------------------------
>
> Key: SPARK-53803
> URL: https://issues.apache.org/jira/browse/SPARK-53803
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib, PySpark
> Affects Versions: 3.5.7
> Reporter: Anand
> Priority: Major
> Labels: pull-request-available
>
> The new components will implement the ARIMA (AutoRegressive Integrated Moving
> Average) algorithm for univariate time series forecasting within the Spark ML
> pipeline API.
> This work will include:
> - Implementation of ARIMA estimator with parameters (p, d, q)
> - A fitted model `ArimaRegressionModel` for prediction
> - Parameter support for (p, d, q) accessible from Scala and Python APIs
> - PySpark bindings under `pyspark.ml.regression`
> - Unit tests in Scala and Python for fit/transform, persistence, and predict
> - An example usage added to `examples/ml/ArimaRegressionExample.scala`
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