Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis

While such transformations are cost-effective, deep learning models. However, the they may be constrained by the quality of the training set and neuroimaging field is notably hampered by the scarcity of may not preserve the temporal dynamics inherent in timeseries such datasets. In this work, we proposed a data augmentation data. Data augmentation can also be facilitated through and validation framework that utilizes dynamic forecasting deep learning models, such as Generative Adversarial with Long Short-Term Memory (LSTM) networks to enrich Networks (GANs), which are capable of generating synthetic datasets. We extended multivariate time series data by fMRI data [6], Additionally, training Recurrent Neural predicting the time courses of independent component Networks (RNNs) to dynamically predict future states serves networks (ICNs) in both one-step and recursive as another method for data augmentation, as demonstrated by configurations.