Weather Prediction from Imbalanced Data Stream using 1D-Convolutional Neural Network
Suja A. Alex, Uttam Ghosh, Nazeeruddin Mohammad · 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22) · 2022
Data stream classification is a complex task in the real world due to its varying characteristics. The most common challenges are concept drift and class imbalance. Concept drift shifts in the underlying function generating the data. The biggest obstacle in achieving an effective classifier is due to class imbalance. In general, batch and ensemble solutions are mainly used to train the classifier with chunks of imbalanced data streams. In the paper, we proposed a framework called Self Organizing Auto-Encoder based 1D-CNN for Weather data forecasting. The objective of this framework is to classify imbalanced non-stationary data streams. A proposed model's concept drift handling strength is assessed using Population Stability Index (PSI). The Weather dataset is used for the evaluation of the proposed model. The results shows improvement in prediction accuracy and the classifier is stable with good PSI.