1. Enhancing algal bloom forecasting: A novel framework for machine learning performance evaluation during periods of special temporal patterns.
- Author
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Xia, Wei, Ilievski, Ilija, and Shoemaker, Christine Ann
- Subjects
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MACHINE learning , *TIME series analysis , *ALGAL blooms , *DEEP learning , *AUTOMATIC identification - Abstract
The evaluation of algal bloom forecasting models typically relies on error metrics that quantify the forecasting performance over the whole test set as a single number. Furthermore, the comparison with simple baseline methods is often omitted. To address this, we introduce a novel framework for Model performance Analysis and Visualization of time series forecasting (MAVts). MAVts incorporates novel algorithms for the automatic identification and visualization of time series periods of interest where the forecasting models are evaluated and compared with simple baseline methods. The application of MAVts on evaluating algal bloom forecasting models composed of sophisticated machine learning (ML) methods, reveals that in 85% of experiments a single error metric is not enough and only in 12.5% of experiments a ML model outperforms all baselines on all metrics and periods of interest. Thus, MAVts emerges as a valuable tool for analyzing and comparing ML models, advancing environmental management and protection. • Introducing MAVts, a framework for analysis of time series forecasting performance. • MAVts provides novel time series period identification and visualization algorithms. • Performance evaluation at various identified time series patterns is informative. • MAVts provides simple baselines with comparable performance to deep learning models. • Past algae concentration is most useful data for algae forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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