We demonstrate that the temporal dictionary ensemble (TDE) is more accurate than other dictionary based approaches.
Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive.
We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF).
The simplest approach to MTSC is to ensemble univariate classifiers over the multivariate dimensions.
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.
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification.
We call this type of approach to TSC dictionary based classification.
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
We demonstrate correctness through equivalence of accuracy on a range of standard test problems and compare the build time of the different implementations.
Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series.
tl;dr: no, it cannot, at least not on average on the standard archive problems.
In 2002, the UCR time series classification archive was first released with sixteen datasets.
This paper introduces and will focus on the new data expansion from 85 to 128 data sets.
We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex.
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.
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.
We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems.
These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive.
Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age.
Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW.