Time collection forecasting is essential in lots of industries, together with finance, healthcare, vitality, and retail. Nonetheless, one of many greatest challenges in time collection modeling is the restricted availability of high-quality coaching knowledge. Not like commonplace machine studying issues, the place huge labeled datasets are sometimes obtainable, time collection knowledge will be sparse, noisy, or seasonally constrained.
That is the place knowledge augmentation comes into play. By artificially growing the scale and variety of coaching knowledge, augmentation methods can considerably enhance forecasting accuracy, mannequin generalization, and robustness.
