Bag-of-Temporal-SIFT-Words for Time Series Classification

Our approach for time series classification builds on two well-known methods in computer vision: local features are extracted from time series using a SIFT- based approach and a

In the experi- mental section, we evaluate the proposed kernel using SIFT features adapted to time series [2, 7] and show that it significantly outperforms the original algorithm

Classifi- cation based on MODIS vegetation index time series is per- formed in order to map crop types in the Brazilian state of Mato Grosso.. We used the recently developed Dense

Our approach for time series classification builds on two well-known methods in computer vision: local features are extracted from time series using a SIFT- based approach and a

It demonstrates two things: firstly, tuning the shapelets then classifying with the LS model (Hybrid-LR) makes no significant difference when compared to ST alone; and secondly,

Since SAX already comes with a distance metric which was demonstrated to yield better performance than the Euclidean distance on real-valued time series data [14], we compare

(3) Multiscale representations are able to reveal time series features at different scales, and a generic classifier is able to conduct feature selection and find important features

Our previous work, Domain Series Corpus (DSCo) [13] for TSC, takes advantage of SAX to compress real-valued time series data into text strings and builds per-class language models as