Hidden Markov models for prediction smoothing in time series classification models

Music City Center 

Predicting hidden states using time-series classification models can result in frequent and erratic switching between the predicted states. This is particularly evident in applications with a high temporal resolution and during transitions between states. In the current investigation, a hidden Markov model (HMM) is fitted to the predicted states from trained time-series classification models to smooth these predictions and eliminate any high-frequency and/or erratic state switching observed in the outcomes. The HMM smoothing approach used in this study is shown to be highly effective at this task and is demonstrated in a case study using both mini-rocket and long-short-term-memory time series state predictions.

prediction smoothing

time series classification

hidden markov model 

Section on Statistical Learning and Data Science