From: University of Maryland
encode time series as different types of images.
reformulate features of time series as visual clues.
three representations for encoding time series as images: Gramian angular summation fields/ Gramian angular difference fields and Markov transition fields.
Recently, researchers are trying to build different network structures from time series for visual inspection or designing distance measures.
build a weighted adjacency matrix is extracting transition dynamics from the first order Markov matrix.
time series ---------> topological properties; but it remains unclear how these topological properties relate to the original time series since they have no exact inverse operations.
time series ----> images ----> tailed CNN for classification
Conclusion:
We aim to further apply our time series models in real world regression/imputation and anomaly detection tasks.
PP: Imaging time-series to improve classification and imputation
原文：https://www.cnblogs.com/dulun/p/12262220.html