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On the efficiency of machine learning algorithms for imputation in spatiotemporal meteorological dataстатья Исследовательская статья
Дата последнего поиска статьи во внешних источниках: 8 сентября 2021 г.
- Авторы: Gorshenin A., Lukina S.
- Журнал: Advances in Intelligent Systems and Computing
- Том: 1315
- Год издания: 2021
- Первая страница: 347
- Последняя страница: 356
- DOI: 10.1007/978-3-030-67133-4_32
- Аннотация: The paper presents comparing various machine learning algorithms in the problem of imputation of missing values in the spatiotemporal precipitation data. Using a special procedure to convert complete data to incomplete one, up to 40% of missing values are artificially placed into the datasets. Then, they are imputed in order to determine the most effective machine learning algorithms with the same hyperparameters for more than hundred worldwide weather stations. A two-step procedure, where the classification results are used to improve regression accuracy, are implemented using Python pro-gramming language. The efficiency of various combinations of methods in-cluding random forests, classic and extreme gradient boosting, support vec-tor machine, EM algorithm are analyzed. It is demonstrated that the best classifier is extreme gradient boosting with average forecasting accuracy of 83.41%. Combination of such methods as XGBClass+XGBoost leads to the best quality of missing values imputation with the normalized RMSE equals from 0.01 to 0.07. All of the above-mentioned methods are tested for the same hyperparameter settings for all weather stations. The novelty of this paper is in the selection of the universal methods for imputation, the accuracies of which are sufficient for processing spatiotemporal meteorological da-ta regardless of their geographic locations even without the fine-tuning. The results obtained allow us to implement methods of computational statistics for detecting extreme precipitation correctly. The presented approaches are also effective for a wider class of observations, for example, environmental data.
- Добавил в систему: Горшенин Андрей Константинович