Abstract:
Time Series Classification (TSC) involves assigning a target label based on features
involving time series data. TSC arises in a variety of domains, like healthcare, finance, process control, weather pattern prediction, etc. This work is focused on
exploiting both frequency and time domains of a time series. Inspired by the
TimesNet proposed in [27], which learns multi-periodic variations, we proposed
Time-Frequency Network (TFNet), a novel Deep Learning model, and applied it
to irregular medical time series data. Earlier methods used either only features
captured in the time domain or in the frequency domain. It is di"cult to learn
both temporal dependencies and understand cyclic or seasonality patterns when
analyzed in a single domain. To tackle these limitations, we extend the TimesNet
model to perform time domain analysis. Our proposed TFNet achieves an improved performance when applied to in-hospital mortality (IHM) prediction based
on 48 hours of ICU stay, on a dataset extracted from Medical Information Mart
for Intensive Care (MIMIC-III)
