Apply noise filters for better forecast performance in Machine Learning

In Machine Learning (ML), one of the crucial tasks is understanding data characteristics to be able to extract exactly relevant information, while noise contained in data can cause misleading estimations and decrease the generalizability of ML-based prediction models. So far, only few previous studies have applied noise filtering techniques when building forecast models. Hence, their efficiency on ML-based forecasts has not yet been comprehensively demonstrated. Therefore, we aim to determine optimal noise filters to enhance the forecast performance of Total Electron Contents (TEC), crustal motion, and Earth’s polar motion. We investigate six noise filtering algorithms (Moving Mean, Moving Median, Lowess, Loess, and Savitzky Golay) on forecast models to select the best-suited filters. Five ML algorithms are applied to train forecast models, that are Support Vector Machine (SVM), Regression Trees, Linear Regression (LR), Ensembles of Trees, and Gaussian Process Regression (GPR). The findings show that the Savitzky Golay algorithm is the most effective on the ML-based forecast models, followed by Loess and Gaussian filters, while Moving Mean is the least sensitive. Noise filters are more sensitive for forecast models based on SVM and LR than Ensembles of Trees and GPR. Applying the Savitzky Golay filter for SVM and LR optimal models can enhance the prediction accuracy up to 14.0 %, 16.1 % and 89.5 % corresponding to forecasting TEC, crustal motion, and Earth's polar motion, respectively; while that for Ensembles and GPR are only from approximate 3.0 to 27.0 %. Overall, using noise filters is one of the practical solutions to improve prediction performance. They can also be used to smoothen time series with variable characteristics and to generalize high-rate data.     «

In Machine Learning (ML), one of the crucial tasks is understanding data characteristics to be able to extract exactly relevant information, while noise contained in data can cause misleading estimations and decrease the generalizability of ML-based prediction models. So far, only few previous studies have applied noise filtering techniques when building forecast models. Hence, their efficiency on ML-based forecasts has not yet been comprehensively demonstrated. Therefore, we aim to determine op...     »