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

arXiv: https://arxiv.org/abs/2312.08383 · HTML: https://mlsanity.com/paper/2312.08383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)

## Built on by (most cited first, 1 of 1)

- [Generative forecasting of brain activity enhances Alzheimer's classification and interpretation](https://mlsanity.com/paper/2410.23515/) (2024)

## Abstract (arXiv)

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.