IEEE Trans Pattern Anal Mach Intell - A Bag-of-Features Framework to Classify Time Series.

Tópicos

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Resumo

Time series classification is an important task with many challenging applications. A nearest-neighbor classifier with dynamic time warping (DTW) distance is a strong solution in this context. On the other hand, feature-based approaches have been proposed as both classifiers and to provide insight into the series, but these approaches have problems handling translations and dilations in local patterns. Considering these shortcomings, we present a framework to classify time series based on a bag-of-features representation (TSBF). Multiple subsequences selected from random locations and of random lengths are partitioned into shorter intervals to capture the local information. Consequently, features computed from these subsequences measure properties at different locations and dilations when viewed from the original series. This provides a feature-based approach that can handle warping (although differently from DTW). Moreover, a supervised learner (that handles mixed data types, different units, etc.) integrates location information into a compact codebook through class probability estimates. Additionally, relevant global features can easily supplement the codebook. TSBF is compared to nearest-neighbor classifiers and other alternatives (bag-of-words strategies, sparse spatial sample kernels, shapelets). Our experimental results show that TSBF provides better results than competitive methods on benchmark datasets from the UCR time series database.

Resumo Limpo

time seri classif import task mani challeng applic nearestneighbor classifi dynam time warp dtw distanc strong solut context hand featurebas approach propos classifi provid insight seri approach problem handl translat dilat local pattern consid shortcom present framework classifi time seri base bagoffeatur represent tsbf multipl subsequ select random locat random length partit shorter interv captur local inform consequ featur comput subsequ measur properti differ locat dilat view origin seri provid featurebas approach can handl warp although differ dtw moreov supervis learner handl mix data type differ unit etc integr locat inform compact codebook class probabl estim addit relev global featur can easili supplement codebook tsbf compar nearestneighbor classifi altern bagofword strategi spars spatial sampl kernel shapelet experiment result show tsbf provid better result competit method benchmark dataset ucr time seri databas

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