Deep learning has firmly established itself as a leading-edge technology across various domains, accompanied by an unprecedented surge in research publications. In the realm of time series classification (TSC), groundbreaking achievements have been reported, setting a new standard on the UCR time series benchmark archive that is beyond the reach of conventional machine learning approaches.
Yet, deep learning research in TSC has encountered worrisome trends. Many seemingly unattainable benchmark results stem from inadequate model training and overfitting the test data, i.e. maximizing the test accuracy of the model over multiple epochs. This is obviously biased and gives an unfair advantage over conventional models/training, yet there has been a concerning influx of approaches published at esteemed venues following this pitfall. Additionally, many approaches undergo evaluation on subsets of the UCR benchmark datasets without transparently indicating the rationale behind such seemingly cherry picking. Finally, many papers do not publish any source codes, though they are based on common frameworks like TensorFlow and PyTorch, which impedes reproducibility.
This seminar is dedicated to addressing common pitfalls that pose a threat to the field of TSC. Throughout the seminar, groups consisting of 2-3 students will select from a list of current time series deep learning models, examine potential pitfalls associated with these models, and attempt to replicate their results.
This process will provide participants with invaluable insights into the current landscape of deep learning for time series classification.
https://hu.berlin/lehre_wbi- Kursverantwortliche/r: Dr. Patrick Schäfer