Bag of recurrence patterns representation 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

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,

4 Symmetry plots (SP) of two time series derived from the logistic map. b) Matrix M 00 of simple recurrences obtained from identity transformation with a non negligible number

When dealing with high spatial resolution images, object-based approaches are generally used in order to exploit the spatial relationships of the data8. However, these

In order to improve the performance of TSC tasks, this disser- tation proposes different methods to transform time series data for a better feature extraction process as well as

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