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Forecasting Time Series - Evaluation Metrics - AutoGluon 1.1.1 documentation

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Picking the right evaluation metric is one of the most important choices when using an AutoML framework. This page lists the forecast evaluation metrics available in AutoGluon, explains when different metrics should be used, and describes how to define custom evaluation metrics. When using AutoGluon, you can specify the metric using the eval_metric argument to TimeSeriesPredictor, for example: AutoGluon will use the provided metric to tune model hyperparameters, rank models, and construct the final ensemble for prediction. Note AutoGluon always reports all metrics in a higher-is-better format. For this purpose, some metrics are multiplied by -1. For example, if we set eval_metric="MASE", the predictor will actually report -MASE (i.e., MASE score multiplied by -1). This means the test_score will be between 0 (most accurate forecast) and − ∞ (least accurate forecast). Currently, AutoGluon supports following evaluation metrics: SQL Scaled quantile loss. WQL Weighted quantile loss. MAE M

Forecasting Time Series - Evaluation Metrics ¶ Picking the right evaluation metric is one of the most important choices when using an AutoML framework. This page lists the forecast evaluation metrics available in AutoGluon, explains when different metrics should be used , and describes how to define custom evaluation metrics . When using AutoGluon, you can specify the metric using the eval_metric argument to TimeSeriesPredictor , for example: from autogluon.timeseries import TimeSeriesPredictor predictor = TimeSeriesPredictor ( eval_metric = "MASE" ) AutoGluon will use the provided metric to t

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