MSTN introduces a lightweight multi-scale temporal network using convolutional encoding, recurrent or attention-based modeling, and gated fusion to achieve claimed state-of-the-art results on 21 of 27 time series benchmarks while using under 1.1M parameters and fast inference.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
MSTN introduces a lightweight multi-scale temporal network using convolutional encoding, recurrent or attention-based modeling, and gated fusion to achieve claimed state-of-the-art results on 21 of 27 time series benchmarks while using under 1.1M parameters and fast inference.