A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency

by   Anthony Bagnall, et al.

sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia, University College London and the Alan Turing Institute. A key initial goal for sktime was to provide time series classification functionality equivalent to that available in a related java package, tsml. We describe the implementation of six such classifiers in sktime and compare them to their tsml equivalents. We demonstrate correctness through equivalence of accuracy on a range of standard test problems and compare the build time of the different implementations. We find that there is significant difference in accuracy on only one of the six algorithms, and this difference was to be expected. We found a much wider range of difference in efficiency. Again, this was not unexpected, but it does highlight ways both toolkits could be improved.



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1 Introduction

sktime111https://github.com//alan-turing-institute/sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia, University College London and the Alan Turing Institute. sktime is designed to provide an unifying API for a range of time series tasks such as anotation, prediction and forecasting. A key prediction component is algorithms for time series classification (TSC). We have implemented a broad range of TSC algorithms. This technical report provides evidence of the correctness of the implementation and assesses the time efficiency of these classifiers by a comparing accuracy and run time to the Java versions222https://github.com/uea-machine-learning/tsml used in an extensive experimental comparison published in 2017 [3]. This is a work in progress, and this document will be updated as we improve and add functionality.

2 sktime structure

sktime is organised into high level packages to reflect the different tasks, shown in Figure 2. We are concerned with the packages classifiers, the components of which make use of distance_measures and transformers. Note that in the dev branch there is a package contrib. This contains implementations that have not been fully integrated yet and bespoke code to run experiments.

Figure 1: Overview of the current sktime file structure (sktime version 0.3).

Classifiers are grouped based on their representation,

Figure 2: Overview of the current sktime classifier packages (sktime version 0.3).

The taxonomy of classifiers is taken from the comparative study presented in [3]. Currently, the following classifiers are available.

interval_based is a package for classifiers built on intervals of the series. Currently, it contains a single classifier in distance_based/tsf.py, Time Series Forest [8]. This is evaluated in Section 3. Spectral based classifiers are located in the package frequency_based. The random interval spectral ensemble (RISE) [17] is the only current concrete implementation of this type ( Section 4). dictionary_based contains classifiers that form histograms of discretised words. Currently, we have the Bag of SFA Symbols (BOSS) [22] classifier, with experiments described in Section 5. shapelets are discriminatory subseries, and classifiers that use them are in the shapelet_based package. We evaluate a version of the shapelet transform classifier [4].

In distance_based/time_series_neigbours.py we have
KNeighborsTimeSeriesClassifier, which can be configured to use a range of distance measures, including: dynamic time warping (DTW); derivative dynamic time warping (DDTW) [10]; weighted DTW [13]; longest common subsequence; edit distance with penalty (ERP); and move-split-merge [25]. This structure is under review, and these nearest neihbour classifiers will not form part of the evaluation in this first version of the benchmark paper. Instead, we evaluate the ElasticEnsemble [16], and ensemble of nearest neighbour classifiers using elastic distance measures, which is in distance_based/elastic_ensemble.py. Furthermore, in distance_based/proximity.py there is the ProximityForest [18], a tree based classifier using a range of distance measures. These are evaluated in Section 6. The sktime experiments described in the following sections can be reproduced using the code in contrib/basic_benchmarking.py.

3 Interval Based: Time Series Forest (TSF) [8]


is probably the simplest bespoke classifier and hence was the first algorithm we implemented. TSF is an ensemble of tree classifiers, each constructed on a different feature space. For any given ensemble member, a set of random intervals is selected, and three summary statistics (mean, standard deviation and slope) are calculated for each interval. The statistics for each interval are concatenated to form the new feature space.

In sktime, there are two ways of constructing a TSF estimator. The hard coded implementation

TimeSeriesForest is in the file
sktime.classifiers.interval_based.tsf. An example usage, including data loading and building, assuming full_path is where the problems reside444 https://github.com/alan-turing-institute/sktime/blob/master/examples/loading_data.ipynb for examples of how to load data, is as follows:

[frame=single] from sktime.utils.load_data import load_from_tsfile_to_dataframe as load_ts import sktime.classifiers.interval_based.tsf as ib #method 1 tsf=ib.TimeSeriesForest(n_trees=100) trainX, trainY = load_ts(full_path + ’_TRAIN.ts’) testX, testY = load_ts(full_path + ’_TEST.ts’) tsf.fit(trainX, trainY) predictions=tsf.predict(testX) probabilities=tsf.predict_proba(testX) Alternatively, you can configure the TimeSeriesForestClassifier in
classifiers/compose/ensemble.py as TSF as follows [frame=single] #method 2 steps = [ (’segment’, RandomIntervalSegmenter(n_intervals=’sqrt’)), (’transform’, FeatureUnion([ (’mean’, RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))), (’std’, RowwiseTransformer(FunctionTransformer(func=np.std, validate=False))), (’slope’, RowwiseTransformer( FunctionTransformer(func=time_series_slope), validate=False)) ])), (’clf’, DecisionTreeClassifier()) ] base_estimator = Pipeline(steps) tsf = TimeSeriesForestClassifier(base_estimator=base_estimator, n_estimators=100) The validate=False is to stop the built in methods using the sktime data format incorrectly (and to suppress annoying warnings). It will be set by default to False in the next release. The configurable version allows for the easy formulation of TSF like variants and other transformation based ensembles. However, this comes at the cost of efficiency. To verify the implementations, we compare the tsml java version555https://github.com/uea-machine-learning/tsml/ used in the bake off [3] to the two sktime versions.

We aim to show there is no difference in accuracy between the three implementations. We evaluate these three classifiers on the default train test split on upto 96 of the UCR archive [6]. For the next release of this paper we will perform the analysis on 30 resamples of the 112 equal length problems of the archive. Full results are available on the associated website666http://www.timeseriesclassification.com/sktime.php. TO DO ADD SPREADSHEET. Figure 3 shows the critical difference diagram [7] for the three versions. It demonstrates there is no significant difference between the classifiers, although rather curiously sktime2 ranks highest.

Figure 3: Average ranks for three TSF implementations. Two from sktime (sktime1 using method 1 and sktime2 using method 2) and one from tsml. There is no significant difference between the three classifiers (as tested with a pairwise wilcoxon test with Holm correction, ).

Figures 4 and 5 shows the pairwise scatter plots for sktime2 vs tsml and sktime1.

Figure 4: TSF sktime1 vs sktime2 scatterplot of test accuracies. sktime2 Wins/Draw/Losses: 44/21/31
Figure 5: TSF tsml vs sktime2 scatterplot of test accuracies. sktime2 Wins/Draw/Losses: 45/15/36

Timing comparison for the three classifiers is shown in Figure 6.

Figure 6: Train time for three TSF classifiers: sktime1 (data1); tsml (data2); and sktime2 (data3).

The configurable version is an order of magnitude slower than the fixed version, which is approximately equivalent to the java based tsml. We are not entirely sure as to the reason for this slowdown with the composite. We suspect it is due to excessive unpacking and packing of data into pandas, but it merits further investigation and detailed profilin, particularly as the accuracy is marginally, although not significantly, higher.

4 Frequency Based: RISE [17]

RISE is similar to TSF in that it is an ensemble of decision tree classifiers. The difference lies in the feature space used for each base classifier. RISE selects a random interval for each base classifier, then transforms the time series in that interval into the power spectrum and autocorrelation features. As with TSF, there are two ways of building RISE.

[frame=single] import sktime.classifiers.interval_based.tsf as ib from sklearn.preprocessing import FunctionTransformer from sklearn.tree import DecisionTreeClassifier from statsmodels.tsa.stattools import acf from sktime.transformers.compose import RowwiseTransformer from sktime.transformers.segment import RandomIntervalSegmenter from sktime.transformers.compose import ColumnTransformer from sktime.transformers.compose import Tabulariser from sktime.pipeline import Pipeline from sktime.pipeline import FeatureUnion from sktime.classifiers.compose import TimeSeriesForestClassifier

#method 1: fixed classifier rise = fb.RandomIntervalSpectralForest(n_trees=100)

[frame=single] #method 2: configurable classifier steps = [ (’segment’,RandomIntervalSegmenter(n_intervals=1, min_length=5)), (’transform’,FeatureUnion([ (’acf’,RowwiseTransformer(FunctionTransformer(func=acf_coefs, validate=False))), (’ps’,RowwiseTransformer(FunctionTransformer(func=powerspectrum, validate=False))) ])), (’tabularise’, Tabulariser()), (’clf’, DecisionTreeClassifier()) ] base_estimator = Pipeline(steps) rise = TimeSeriesForestClassifier(base_estimator=base_estimator, n_estimators=100)

def acf_coefs(x, maxlag=100): x = np.asarray(x).ravel() nlags = np.minimum(len(x) - 1, maxlag) return acf(x, nlags=nlags).ravel()

def powerspectrum(x, **kwargs): x = np.asarray(x).ravel() fft = np.fft.fft(x) ps = fft.real * fft.real + fft.imag * fft.imag return ps[:ps.shape[0] // 2].ravel()

The composite version requires the definition of the acf and power spectrum functions. We will make this better encapsulated in the next release. To validate these implementations, we again benchmark against the tsml Java version, using RISE with 50 trees. Using the built in numpy functions for sktime2 creates a problem with some datasets: if a zero variance interval is passed to the numpy functions, an exception is thrown. In sktime1 and tsml we can manually adjust for this circumstance. The nature of the composite sktime2 makes this harder to manage. This means we are only able to compare on 68 datasets. Figures 

78 and 9 show there is no significant difference in accuracy between the three classifiers.

Figure 7: Average ranks for three RISE implementations. Two from sktime (sktime1 using method 1 and sktime2 using method 2) and one from tsml. There is no significant difference between the three classifiers (as tested with a pairwise wilcoxon test with Holm correction, ).
Figure 8: RISE sktime2 vs sktime1 scatterplot of test accuracies. sktime2 Wins/Draw/Losses: 34/5/29
Figure 9: RISE tsml vs sktime2 scatterplot of test accuracies. sktime2 Wins/Draw/Losses: 25/5/38

The timing comparison for RISE, shown in Figure 10, highlights two curious characteristics. Firstly, sktime2 is an order of magnitude faster than both sktime1 and tsml. We believe this is due to to the efficient numpy implementations used, although it merits further investigation. Secondly, tsml has much higher variance than the sktime implementations, and seems to take a very long time on some unexpectedly small problems. This also merits further investigation.

Figure 10: Train time for three RISE classifiers: sktime1 (data2); tsml (data3); and sktime2 (data1).

5 Dictionary Based: BOSS [22]

The BOSS implementation contains two components, BOSSIndividual and the BOSSEnsemble (what we refer to as BOSS). Individual BOSS classifiers create a histogram of words from each series using the SFA transform over sliding windows. A one nearest neighbour classifier using a bespoke BOSS distance is then used for classification. The BOSS Ensemble is an ensemble of BOSS Individual classifiers. The ensemble members are selected through a grid search of individual BOSS parameters, with classifiers below 92% accuracy of the best classifier removed.

We also implement a faster and more configurable version of the classifier cBOSS [20], replacing the grid search with a filtered random selection. Use of this alternative method can be ensembled using the BOSSEnsemble randomised_ensemble parameter. Two parameters are required to be set for this version, the number of individual BOSS classifiers to be built n_parameter_samples and the max number of classifiers in the ensemble max_ensemble_size. cBOSS is also contractable, allowing a unit of time in minutes using the time_limit parameter to replace the n_parameter_samples.

Examples of how to construct BOSS and cBOSS are demonstrated in the following code sample.

[frame=single] import sktime.classifiers.dictionary_based.boss as db

# By default the original BOSS algorithm is setup boss = db.BOSSEnsemble()

# Configuration for recommended cBOSS settings c_boss = db.BOSSEnsemble(randomised_ensemble=True, n_parameter_samples=250, max_ensemble_size=50)

# cBOSS contracted for 1 hour, input time must be in minutes c_boss_contract = db.BOSSEnsemble(randomised_ensemble=True, time_limit=60, max_ensemble_size=50)

Our primary goal is to test the correctness and efficiency of the standard implementations. To this end, we compare the implementations of BOSS in sktime and tsml. Figure 11 plots the accuracy of the two classifiers on 42 datasets. The differences are insignificant. However, the timing results displayed in Figure 12 show how much slower the sktime implementation is: approximately 10-40 times slower than tsml. This was not unexpected. The implementation is mapped directly from Java and does not exploit any of the efficiency improvements that can be used in python. This is required future work.

Figure 11: BOSS sktime vs tsml scatterplot of test accuracies. sktime Wins/Draw/Losses: 5/30/7
Figure 12: Train time for two BOSS classifiers: sktime (data2) and tsml (data1).

6 Distance Based: Elastic Ensemble (EE) and Proximity Forest (PF)

The Elastic Ensemble (EE) [16] ensembles 11 different distance measures each coupled with a 1-nearest-neighbour (NN) classifier, together known as the constituents of EE. A distance measure enables the comparison of similarity of two time series, therefore can be employed in a NN classifier. The variation in distance measure of each constituent injects diversity into the ensemble and provides superior classification performance over any individual constituent alone. EE uses the following distance measures: Euclidean distance, Dynamic Time Warping (DTW), Derivative DTW (DDTW) [15], DTW with cross-validated warping window (DTWCV) [21], DDTW with cross-validated warping window (DDTWCV), Lowest Common Sub-Sequence (LCSS) [12], Edit Distance with Real Penalty (ERP) [5], Move-Split-Merge (MSM) [25], and Time Warp Edit Distance (TWED) [19]

. The latter eight of these distance measures require hyperparameters to be set for reliable similarity measurement. EE employs a tuning process for each constituent over 100 parameter options using leave-one-out-cross-validation (LOOCV).

Comparing long time-series using distance measures can be time consuming, therefore both a cython and python version are provided in sktime. Further, sktime provides an extension to scikit-learn’s KNeighborsTimeSeriesClassifier to enable usage with pandas dataframes and custom distance measures. The tuning of each constituent of EE can be time consuming also, therefore we provide capability to reduce the number of neighbours used in examination of parameter options and reduce the number of parameters per constituent overall. Both of these have been shown to significantly speed-up EE with no significant loss in classification performance.

[frame=single] from sktime.classifiers.distance_based.elastic_ensemble import ElasticEnsemble from sktime.classifiers.distance_based.time_series_neighbors import KNeighborsTimeSeriesClassifier from sklearn.model_selection import GridSearchCV, LeaveOneOut

# 1NN classifier with full DTW as the distance measure dtw_nn = KNeighborsTimeSeriesClassifier(metric=’dtw’, n_neighbors=1)

# 1NN classifier tuned for best accuracy over 100 DTW # warping window options dtwcv_nn = GridSearchCV( estimator=KNeighborsTimeSeriesClassifier(metric=’dtw’, n_neighbors=1), param_grid=’metric_params’: [’w’: x / 100 for x in range(0, 100)] cv=LeaveOneOut(), scoring=’accuracy’ )

# Default EE with full tuning effort ee = ElasticEnsemble()

# Configuration for reducing tuning efforts of the EE ee = ElasticEnsemble(proportion_of_param_options = 0.5, proportion_of_train_in_param_finding = 0.1)

Proximity Forest (PF) [18]

is a competitor to EE and implements a forest of decision trees, known as Proximity Trees (PT), in a Random Forest like structure. A Proximity Tree (PT) partitions data by comparing the relative similarity of instances against a set of exemplar instances. At any given node in the PT, a one exemplar instance is randomly picked per class from the data passed down from the parent node. The data is partitioned based upon the similarity of data instances against exemplar instances, grouping similar instances. Each group of instances, along with the corresponding exemplar, are passed down as input data to a child node and the process repeats until leaf nodes are pure. This process is known as splitting.

The similarity of instances against exemplar instances is determined using distance measures. PF uses the same distance measures and corresponding parameter ranges as EE. Each split in a PT randomly chooses the distance measure and corresponding parameter set if required. This randomness can lead to poor splits, therefore a hyperparameter controls the evaluation of multiple splits at each node, choosing the best split based upon gini score. Each split can be seen as a PT of height 1, otherwise known as a Proximity Stump (PS).

The PF generates a set number of PTs and combines their predictions using a majority vote. The extreme randomisation across the PF produces classifier which can be built in a fraction of the time of EE and produce competitive prediction performance.

[frame=single] from sktime.classifiers.distance_based.proximity_forest import ProximityForest, ProximityStump, ProximityTree

# Proximity Stump (PT with 1 level of depth) ps = ProximityStump()

# Proximity Tree with 5 split evaluations per tree node pt = ProximityTree(n_stump_evaluations = 5)

# Proximity Forest with 100 trees and # 5 split evaluations per tree node pf = ProximityForest(n_trees = 100, n_stump_evaluations = 5)

7 Shapelet Based: Shapelet Transform Classifier (STC) [11]

The shapelet transform classifier is a single pipeline of a shapelet transform (transformers/shapelets.py) and a classifier (by default, a random forest with 500 trees). The original shapelet transform performed a complete enumeration of the shapelet space, and used a heterogeneous ensemble for a classifier [11]. More recent research has shown that equivalent accuracy can be obtained through randomly searching for shapelets rather than a full enumeration [4]. The shapelet transform in sktime is contractable, in that you can specify an amount of time to search for shapelets before performing the transform. The default is 300 minutes. The latest STC no longer uses a rotation forest classifier, since it has been shown to be best for this type of problem [2]. Rotation forest is not yet available in python (we are working on an alpha version), hence we use random forest for our comparison. We run STC in sktime and tsml with a 1000 minute time limit and use the sklearn amd Weka random forest implementations as the final classifier.

8 Conclusions and Future Directions

Developing time series classification algorithms is an active research field. Our goal in providing sktime is to make researching this field easier and more transparent. If researchers use a common framework, reproducability and comparison to the current state of the art becomes easy. In this way we hope to drive the field forward to help automatically answer the key questions for classifying time series: which is the best algorithm for the task at hand.

The classification component of sktime has broad functionality, but there is much that could be added and improved. Our work plan for the medium term is as follows.

8.1 Improved Classifier Functionality

It has taken a surprising amount of effort to get this far, but there is still much to do. This exercise has highlighted several issues with sktime that need resolving before the next release to master.

  1. interval based: why is the composite version so much slower than the others?

  2. frequency based: can we speed up RISE through matrix operations without exception being continually thrown?

  3. dictionary based: can we speed up BOSS through a more pythonesque implementation?

  4. shapelet based: can we improve accuracy by using rotation forest and improving the search efficiency through cython?

  5. Can we improve EE performance through restricting the parameter search?

We also intend to improve the functionality by making all classifiers contractable and allow for efficient estimation of the test error from the train data. In addition to improving the existing set of classifiers in these ways, we intend porting in recently proposed alternative TSC algorithms such as FastEE, TS-CHIEF [24], WEASEL [23], Shapelet Forest [14] and HIVE-COTE [17]. We have implemented the range of distance functions and kernels described in [1] and the Matrix Profile distance MPDist [9]. We will evaluate these classifiers to examine how they perform in relation to those already implemented within the toolkit.

8.2 Handle Unequal Length Series

One of the primary motivators for our data model of pandas of series objects was to facilitate the easy incorporation of unequal length time series as classification problems. The current suite of available algorithms needs to be adjusted to allow for this use case. For some algorithms this will be fairly simple: BOSS, for example, can simply normalise the histograms and shapelets are independent of series length. However, these design choices may have an impact on accuracy and experimentation using the enhancements will be required.

8.3 Multivariate Time Series Classifiers

sktime already provides two composition interfaces for solving multivariate time series classification problems, including

  • the ColumnConcatenator, a transformer that can be used to concatenate two or more time series columns into a single long time series column, so that one can then apply a classifier to the concatenated, univariate data, and

  • the ColumnEnsembleClassifier, a meta-estimator that allows for column-wise ensembling in which a separate classifier is fitted for each time series column and their predictions are aggregated.

We have started to implement bespoke methods for specific estimators to handle multivariate time series input data and we aim to add more bespoke methods in future work, e.g. finding shapelets in multidimensional space or extending time series forest to extract features from multidimensional segments (or hyper-planes). There are also a multitude of bespoke methods for classifying time series. We shall draw up a candidate

8.4 Time series regression

The composite structure of many time series classifiers allows us to refactor them into their regressor counterparts. We have started implementing regression algorithms, including a TSF regressor and are currently working on adding more regressors.


  • [1] A. Abanda, U. Mori, and J. Lozano. A review on distance based time series classification. Data Mining and Knowledge Discovery, 33(2):378–412, 2019.
  • [2] A. Bagnall, A. Bostrom, G. Cawley, M. Flynn, J. Large, and J. Lines. Is rotation forest the best classifier for problems with continuous features? ArXiv e-prints, arXiv:1809.06705, 2018.
  • [3] A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 31(3):606–660, 2017.
  • [4] A. Bostrom and A. Bagnall. Binary shapelet transform for multiclass time series classification. Transactions on Large-Scale Data and Knowledge Centered Systems, 32:24–46, 2017.
  • [5] L. Chen and R. Ng. On the marriage of Lp-norms and edit distance. In Proc. 30th International Conference on Very Large Databases (VLDB), 2004.
  • [6] H. Dau, A. Bagnall, K. Kamgar, M. Yeh, Y. Zhu, S. Gharghabi, and C. Ratanamahatana. The UCR time series archive. ArXiv e-prints, arXiv:1810.07758, 2018.
  • [7] J. Demšar. Statistical comparisons of classifiers over multiple data sets.

    Journal of Machine Learning Research

    , 7:1–30, 2006.
  • [8] H. Deng, G. Runger, E. Tuv, and M. Vladimir.

    A time series forest for classification and feature extraction.

    Information Sciences, 239:142–153, 2013.
  • [9] S. Gharghabi, S. Imani, A. Bagnall, A. Darvishzadeh, and E. Keogh. Matrix Profile XII: MPDist: A novel time series distance measure to allow data mining in more challenging scenarios. In Proc. 18th IEEE International Conference on Data Mining, 2018.
  • [10] T. Górecki and M. Łuczak. Using derivatives in time series classification. Data Mining and Knowledge Discovery, 26(2):310–331, 2013.
  • [11] J. Hills, J. Lines, E. Baranauskas, J. Mapp, and A. Bagnall. Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery, 28(4):851–881, 2014.
  • [12] D. Hirschberg. Algorithms for the longest common subsequence problem. Journal of the ACM, 24(4):664–675, 1977.
  • [13] Y. Jeong, M. Jeong, and O. Omitaomu. Weighted dynamic time warping for time series classification. Pattern Recognition, 44:2231–2240, 2011.
  • [14] I. Karlsson, Papapetrou P, and H. Boström. Generalized random shapelet forests. Data Mining and Knowledge Discovery, 30(5):1053–1085, 2016.
  • [15] E. Keogh and M. Pazzani. Derivative dynamic time warping. In Proc. 1st SIAM International Conference on Data Mining (SDM), 2001.
  • [16] J. Lines and A. Bagnall. Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery, 29:565–592, 2015.
  • [17] J. Lines, S. Taylor, and A. Bagnall. Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles. ACM Trans. Knowledge Discovery from Data, 12(5), 2018.
  • [18] B. Lucas, A. Shifaz, C. Pelletier, L. O’Neill, N. Zaidi, B. Goethals, F. Petitjean, and G. Webb. Proximity forest: an effective and scalable distance-based classifier for time series. Data Mining and Knowledge Discovery, 33(3):607–635, 2019.
  • [19] P. Marteau. Time warp edit distance with stiffness adjustment for time series matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):306–318, 2009.
  • [20] M. Middlehurst, W. Vickers, and A. Bagnall. Scalable dictionary classifiers for time series classification. arXiv preprint arXiv:1907.11815, 2019.
  • [21] C. Ratanamahatana and E. Keogh. Three myths about dynamic time warping data mining. In Proc. 5th SIAM International Conference on Data Mining, 2005.
  • [22] P. Schäfer. The BOSS is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery, 29(6):1505–1530, 2015.
  • [23] P. Schäfer and U. Leser. Fast and accurate time series classification with weasel. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 637–646. ACM, 2017.
  • [24] A. Shifaz, C. Pelletier, F. Petitjean, and G. Webb. TS-CHIEF: A scalable and accurate forest algorithm for time series classification. ArXiv e-prints, arXiv:1906.10329, 2019.
  • [25] A. Stefan, V. Athitsos, and G. Das. The Move-Split-Merge metric for time series. IEEE Transactions on Knowledge and Data Engineering, 25(6):1425–1438, 2013.