  Data Augmentation (DA) has emerged as an indispensable strategy in Time
Series Classification (TSC), primarily due to its capacity to amplify training
samples, thereby bolstering model robustness, diversifying datasets, and
curtailing overfitting. However, the current landscape of DA in TSC is plagued
with fragmented literature reviews, nebulous methodological taxonomies,
inadequate evaluative measures, and a dearth of accessible, user-oriented
tools. In light of these challenges, this study embarks on an exhaustive
dissection of DA methodologies within the TSC realm. Our initial approach
involved an extensive literature review spanning a decade, revealing that
contemporary surveys scarcely capture the breadth of advancements in DA for
TSC, prompting us to meticulously analyze over 100 scholarly articles to
distill more than 60 unique DA techniques. This rigorous analysis precipitated
the formulation of a novel taxonomy, purpose-built for the intricacies of DA in
TSC, categorizing techniques into five principal echelons:
Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and
Automated Data Augmentation. Our taxonomy promises to serve as a robust
navigational aid for scholars, offering clarity and direction in method
selection. Addressing the conspicuous absence of holistic evaluations for
prevalent DA techniques, we executed an all-encompassing empirical assessment,
wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR
time-series datasets, employing ResNet and a multi-faceted evaluation paradigm
encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a
benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the
inconsistent efficacies of DA techniques, with....
