Enhancing Time Series Data Predictions: A Survey of Augmentation Techniques and Model Performances

Abstract

Machine Learning approaches are excellent but require a large amount of data which is not easy to get. Data augmentation approaches are used to generate data and improve models’ performance. This study investigates the efficacy of machine learning models in temperature prediction within the domain of climate research, addressing the challenge of limited data through the incorporation of data augmentation techniques. Using a variety of traditional statistical and machine learning models on the "Jena Climate 2009-2016" dataset, this study examines how well data augmentation techniques can increase the accuracy of temperature predictions. Climate research and meteorology are two domains where temperature prediction is essential. However, because climate data is complex and dynamic, getting high precision in such projections is still a difficult task. We assess the efficacy of various models, including the traditional statistical ARIMA model, deep learning models like WaveNet and recurrent neural networks like LSTM, in conjunction with various data augmentation techniques, to address this difficulty. When rotation augmentation and jittering are applied, the ARIMA model significantly improves, indicating how well-suited traditional time series models are to fluctuations in data. Deep learning models, such as LSTM and WaveNet, on the other hand, show strong baseline performance when no augmentation is applied. The analysis reveals that unlike permutation techniques, scaling, Jittering and Rotation consistently improve model performance, emphasizing the critical role of selecting appropriate data augmentation strategies. WaveNet exhibits remarkable consistency in accuracy and precision, surpassing ARIMA, while LSTM demonstrates strong performance even with augmentations. These results offer a thorough understanding of how model design and data augmentation methods interact when predicting time series data. By providing insights that can improve temperature forecast accuracy and advance knowledge in meteorology and related fields, this research adds to the changing field of time series forecasting.

Topics & Concepts