  Deep neural networks, including transformers and convolutional neural
networks, have significantly improved multivariate time series classification
(MTSC). However, these methods often rely on supervised learning, which does
not fully account for the sparsity and locality of patterns in time series data
(e.g., diseases-related anomalous points in ECG). To address this challenge, we
formally reformulate MTSC as a weakly supervised problem, introducing a novel
multiple-instance learning (MIL) framework for better localization of patterns
of interest and modeling time dependencies within time series. Our novel
approach, TimeMIL, formulates the temporal correlation and ordering within a
time-aware MIL pooling, leveraging a tokenized transformer with a specialized
learnable wavelet positional token. The proposed method surpassed 26 recent
state-of-the-art methods, underscoring the effectiveness of the weakly
supervised TimeMIL in MTSC. The code will be available at
https://github.com/xiwenc1/TimeMIL.
