Time series often exhibit limited data availability, irregular sampling, and high-dimensional structure, making augmentation and feature selection central to building robust neural forecasting models. This chapter surveys state-of-the-art neural network-driven approaches for expanding and refining temporal datasets. We examine transformation-based and frequency-domain augmentations, reinforcement-learning policies for adaptive perturbations, and modern generative frameworks—including diffusion models and foundation models—that synthesize realistic temporal trajectories under complex dynamics. Complementing augmentation, we review classical and deep feature selection methods that identify salient variables, suppress redundancy, and promote interpretability in high-dimensional settings. By integrating mathematical formulations, empirical insights, and comparative evaluation, the chapter establishes a unified perspective on how advanced augmentation and feature selection pipelines can enhance the efficiency, reliability, and explainability of neural models for time series analysis.