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Found 12 papers, 2 papers with code
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
We demonstrate that the temporal dictionary ensemble (TDE) is more accurate than other dictionary based approaches.
HIVE-COTE 2.0: a new meta ensemble for time series classification
Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive.
The Canonical Interval Forest (CIF) Classifier for Time Series Classification
We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF).
Detecting Electric Devices in 3D Images of Bags
Given the massive volume of luggage that needs to be screened for this threat, the best way to automate the detection is to first filter whether a bag contains an electric device or not, and if it does, to identify the number of devices and their location.
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification.
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers
We call this type of approach to TSC dictionary based classification.
Can automated smoothing significantly improve benchmark time series classification algorithms?
tl;dr: no, it cannot, at least not on average on the standard archive problems.
The UEA multivariate time series classification archive, 2018
In 2002, the UCR time series classification archive was first released with sixteen datasets.
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex.
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts
We show that the Heterogeneous Ensembles of Standard Classification Algorithms (HESCA), which ensembles based on error estimates formed on the train data, is significantly better (in terms of error, balanced error, negative log likelihood and area under the ROC curve) than its individual components, picking the component that is best on train data, and a support vector machine tuned over 1089 different parameter configurations.
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
We describe what results we expected from each class of algorithm and data representation, then observe whether these prior beliefs are supported by the experimental evidence.
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive.
