Mitigating Drift in Time Series Data with Noise Augmentation

• Published in: 2019 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 227 - 230
• Main Authors: Fields, Tonya, Hsieh, George, Chenou, Jules
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Adaptation models; Atmospheric modeling; data drift; Data models; machine learning; Machine learning algorithms; Predictive models; time series; Time series analysis; Training

Machine leaning (ML) models must be accurate to produce quality AI solutions. There must be high accuracy in the data and with the model that is built using the data. Online machine learning algorithms fits naturally with use cases that involves time series data. In online environments the data dist...