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Benchmarking time series classification – Functional data vs machine learning approaches

by   Florian Pfisterer, et al.

Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal with the analysis of data that is continuously indexed over some domain. While often employing different methods, both fields strive to answer similar questions, a common example being classification or regression problems with functional covariates. We study methods from functional data analysis, such as functional generalized additive models, as well as functionality to concatenate (functional-) feature extraction or basis representations with traditional machine learning algorithms like support vector machines or classification trees. In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners. Additionally, we provide a software framework in R for functional data analysis for supervised learning, including machine learning and more linear approaches from statistics. This allows convenient access, and in connection with the machine-learning toolbox mlr, those methods can now also be tuned and benchmarked.


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1 Introduction

The analysis of functional data is becoming more and more important in many areas of application such as medicine, economics, or geology (cf. Ullah and Finch (2013), Wang et al. (2016)), where this type of data occurs naturally. In industry, functional data are often a by-product of continuous monitoring of production processes, yielding great potential for data mining tasks. A common type of functional data are time series, as time series can often be considered as discretized functions over time.

Many researchers publish software implementations of their algorithms, therefore simplifying the access to already established methods. Even though such a readily available, broad range of methods to choose from is desirable in general, it also makes it harder for non-expert users to decide which method to apply to a problem at hand and to figure out how to optimize their performance. As a result, there is an increasing demand for automated model selection and parameter tuning.

Furthermore, the functionality of available pipeline steps ranges from simple data structures for functional data, to feature extraction methods and packages offering direct modeling procedures for regression and classification. Users are again faced with a multiplicity of software implementations to choose from and, in many instances, combining several implementations may be required. This can be difficult and time-consuming, since the various implementations utilize a multiplicity of different workflows which the user needs to become familiar with and synchronize in order to correctly carry out the desired analysis.

There is a wide variety of packages for functional data analysis in R R Core Team (2014) available that provide functionality for analyzing functional data. Examples range from the fda (Ramsay et al., 2018) package which includes object types for functional data and allows for smoothing and simple regression, to, e.g., boosted additive regression models for functional data in FDboost (Brockhaus and Ruegamer, 2018). For an extensive overview, see the CRAN task view (Scheipl, 2018).

Many of those packages are designed to provide algorithmic solutions for one specific problem, and each of them requires the user to become familiar with its user interface. Some of the packages, however, such as fda.usc (Febrero-Bande and Oviedo de la Fuente, 2012) or refund (Goldsmith et al., 2018) are not designed for only one specific analysis task, but combine several approaches. Nevertheless, these packages do not offer unified frameworks or consistent user interfaces for their various methods, and most of the packages can still only be applied separately.

A crucial advantage of providing several algorithms in one package with a unified and principled user interface is that it becomes much easier to compare the provided methods with the intention to find the best solution for a problem at hand. But to determine the best alternative, one still has to be able to compare the methods at their best performance on the considered data, which requires hyperparameter search and, more preferably, efficient tuning methods.

While the different underlying packages are often difficult and sometimes even impossible to extend to new methods, custom implementations and extensions can be easily included in the accompanying software.

We want to stress that the focus of this paper does not lie in proposing new algorithms for functional data analysis. Its added value lies in a large comparison of algorithms while providing a unified and easily accessible interface for combining statistical methods for functional data with the broad range of functions provided by mlr, most importantly benchmarking and tuning. Additionally, the often overlooked possibility of extracting non-functional features from functional data is integrated, which enables the user to apply classical machine learning algorithms such as support vector machines (Cortes and Vapnik, 1995) to functional data problems.

In a benchmark study similar to Bagnall et al. (2017a) and Fawaz et al. (2019), we explore the performance of implemented methods, and try to answer the following questions:

  1. Can functional data problems be solved with classical machine learning methods ignoring the functional structure of the data as well as with more elaborate methods designed for this type of data? Bagnall et al. (2017a) measure the performance of some non-functional-data-specific algorithms such as the rotation forest (Rodriguez et al., 2006), but this does not yield a complete picture.

  2. Guidance on the wide range of available algorithms is often hard to obtain. We aim to make some recommendations in order to simplify the choice of learning algorithm.

  3. Do statistical methods explicitly tailored to the analysis of functional data (e.g. FDboost, Brockhaus and Ruegamer, 2018) perform well on classical time series tasks? No benchmark results for these methods, which provide interpretable results, are currently available.

  4. Many methods that represent functional data in a non-functional domain have been proposed and are also often applied in practice. Examples for this include either hand crafted features (cf. Stachl and Bühner, 2015), summary statistics (Hyndman et al., 2018), or generally applicable methods such as wavelet decomposition (Aldrich, 2013).

  5. Hyperparameter optimization is a very important step in many machine learning applications. In our benchmark, we aim to quantify the impact of hyperparameter optimization for a set of given algorithms on several data sets.


As contributions of this paper, we aim to answer the questions posed above. Additionally, we provide a toolbox for the analysis of functional data. It implements several methods for feature extraction and directly modeling functional data, including a thorough benchmark of those algorithms. This toolbox also allows for full or partial replication of the conducted benchmark comparison.

2 Related Work

In the remainder of the paper, we focus on comparing algorithms from the functional data analysis and the machine learning domain. Functional data analysis traditionally values interpretable results and valid statistical inference over prediction quality. Therefore functional data algorithms are often not compared with respect to their predictive performance in literature. We aim to close this gap. On the other hand, machine learning algorithms often do not yield interpretable results. While we consider both aspects to be important, we want to focus on predictive performance in this paper.

2.1 Feature extraction and classical machine learning methods

In this work, we differentiate between machine learning algorithms that can directly be applied to functional data, and algorithms intended for scalar features, which we call classical machine learning methods.

A popular approach when dealing with functional data is to reduce the problem to a non-functional task by extracting relevant non-functional features (Ullah and Finch, 2013). Applying classical machine learning methods after extracting meaningful features can then lead to competitive results (cf. Goldsmith and Scheipl, 2014, e.g.)

or at least provide baselines, which are in general not covered by functional data frameworks. In our framework, such functionality is easily available by combining feature extraction, e.g., based on extracting heuristic properties

(cf. tsfeatures; Hyndman et al., 2018) or wavelet coefficients (Mallat, 1989; Aldrich, 2013) and analyzing these derived scalar features with classical machine learning tools provided by mlr.

Based on some existing functionality of the listed packages, we adapt different feature extraction methods. Along with different algorithms already proposed in literature, we propose two new custom methods, DTWKernel and MultiResFeatures:

  • tsfeatures (Hyndman et al., 2018) extracts scalar features, such as auto-correlation functions, entropy and other heuristics from a time series.

  • fourier

    transforms data from the time domain into the frequency domain using the

    fast fourier transform

    (Brigham and Morrow, 1967). Extracted features are either phase or amplitude coefficients.

  • bsignal B-Spline representations from package FDboost Brockhaus and Ruegamer (2018)

    are used as feature extractors. Given the knots vector and effective degree of freedom, we extract the design matrix for the functional data using


  • wavelets (Aldrich, 2013) applies a discrete wavelet transform to time series or functional data, e.g., with Haar or Daubechies wavelets. The extracted features are wavelet coefficients at several resolution levels.

  • PCA

    projects the data on their principal component vectors. Only a subset of the principal component scores representing a given proportion of signal variance is retained.

  • DTWKernel computes the dynamic time warping distances of functional or time series data to (a set of) reference data. We implement dynamic time warping (DTW) based feature extraction. This method computes the dynamic time warping distance of each observed function to a (user-specified) set of reference curves. The distances of each observation to the reference curves is then extracted as a vector-valued feature. The reference curves can either be supplied by the user, e.g., they could be several typical functions for the respective classes, or they can be obtained from the training data. In order to compute dynamic time warping distances, we use a fast dynamic time warping (Rakthanmanon et al., 2012) implementation from package rucrdtw (Boersch-Supan, 2016).

  • MultiResFeatures extracts features, such as the mean at different levels of resolution (zoom-in steps). Inspired by the image pyramid and wavelet methods, we implement a feature extraction method, multi-resolution feature extraction where we extract features like mean and variance computed over specified windows of varying widths. Starting from the full sequence, the sequence is repeatedly divided into smaller pieces, where at each resolution level, a scalar value is extracted. All extracted features are concatenated to form the final feature vector.

2.2 Methods for functional data

Without feature extraction, direct functional data modeling (both classification and regression) methods incorporated in our package span two families: The first family of semi-parametric approaches includes FGAM (Goldsmith et al., 2018), FDboost (Brockhaus and Ruegamer, 2018), and the functional generalized linear model (FGLM; Ramsay, 2006)

, which are all structured additive models. Those methods use (tensor product) spline basis functions or functional principal components (FPCs)

(Srivastava and Klassen, 2016)

to represent effects fitted in a generalized additive model. While FGAM and FGLM use the iterated weighted least square (IWLS) method to generate maximum likelihood estimates, FDboost uses component-wise gradient boosting

(Hothorn et al., 2010) to optimize the parameters. Additionally, the estimated parameters can be penalized. A general formula for this family of methods is , where

represents a functional of the conditional response distribution (e.g., an expectation or a quantile),

is a vector of (functional) covariates and are partial additive effects of subsets of in basis function representation, cf. Greven and Scheipl (2017) for a general introduction.

The second family of methods are non-parametric methods as introduced in Ferraty and Vieu (2006), e.g., based on (semi-)metrics which quantify local or global differences or distances across curves. For example, the distance between two instances could be defined by the distance of two curves . Kernel functions are used to average over the training instances and weigh their respective contributions based on the value of their distance semi-metric to the predicted instance. Functional -nearest neighbors algorithms can also be defined based on such semi-metrics. Implementations can be found in packages fda.usc (Febrero-Bande and Oviedo de la Fuente, 2012) and classiFunc (Maierhofer and Pfisterer, 2018).

2.3 Toolboxes for functional data analysis

The package fda (Ramsay et al., 2018) contains several object types for functional data and allows for smoothing and regression for functional data. Analogously, the R-package fda.usc (Febrero-Bande and Oviedo de la Fuente, 2012) contains several classification algorithms that can be used with functional data. In Python, scikit-fda (Grupo de Aprendizaje Automatico - Universidad Autonoma de Madrid, 2019) offers both representation of and (pre-)processing methods for functional data, but only a very small set of machine learning methods for classification or regression problems is implemented at the time of writing.

As a byproduct of the Time-Series Classification Bake-off Bagnall et al. (2017a), a wide variety of algorithms were implemented and made available. But this implementation emphasizes the benchmark over providing a data analysis toolbox for users, and is therefore not easily usable for inexperienced users.

2.4 Benchmarks

The recently published benchmark analysis Time-Series Classification Bake-off by Bagnall et al. (2017a) provides an overview of the performance of state-of-the-art algorithms for time series classification. They re-implement (in Java) and compare 18 algorithms designed especially for time series classification on 85 benchmark time series data sets from Bagnall et al. (2017b). In their analysis, they also include results from several standard machine learning algorithms. They note that the rotation forest (Rodriguez et al., 2006) and random forest (Breiman, 2001) are competitive with their time series classification baseline (1-nearest neighbor with dynamic time warping distance; Tormene et al., 2008). Their results show that ensemble methods such as collection of transformation ensembles (COTE; Bagnall et al., 2015) perform best, but for the price of considerable runtime.

Deep learning methods applied to time series classification tasks have also shown competitive prediction power. For example, Fawaz et al. (2019) provide a comprehensive review of state-of-the-art methods. The authors compared both generative models and discriminative models, including

fully connected neural networks, convolutional neural networks, auto-encoders

and echo state networks, whereas only discriminative end-to-end approaches were incorporated in the benchmark study.

The benchmark study conducted in this work does not aim to replicate or compete with earlier studies like (Bagnall et al., 2017a), but instead tries to extend their results.

3 Functional Data

In contrast to non-functional data analysis, where the measurement of a single observation is a vector of scalar components whose entries represent values of the separate multidimensional features, functional data analysis treats and analyses the features themselves as functions over their domain. By learning to represent the underlying function, the carried out analysis is not just restricted to the measured discrete values but it is possible to sample from (and analyze) the entire domain space.

In this work, we focus on pairs of features and corresponding labels for supervised learning. In contrast to non-functional data analysis, where the measurement of a single observation is a vector of scalar components, functional features are function-valued over their domain. The features can thus also be a function, i.e., , . In practice, functional data comes in the form of observed values , where each corresponds to a discrete point on the continuum. Those observed values stem from an underlying function evaluated over a set of points. A frequent type of functional data is time series data, i.e., measurements of a process measured at discrete time-points.

For example, in some electrical engineering applications, signals are obtained over time at a certain sampling rate, but other domains are possible as well. Spectroscopic data, for example, are functional data recorded over certain parts of the electromagnetic spectrum. One such example is depicted in Figure 1. It shows spectroscopy data of fossil fuels (Fuchs et al., 2015) where the measured signal represents reflected energies in the ultraviolet-visible (UV-VIS) and the near infrared spectrum (NIR). In the plot, different colors correspond to different instances. This is a typical example of a scalar-on-function regression problem, where the inputs are a collection of spectroscopic curves for a fuel, and the prediction target is the heating value of the fossil fuel.

In Figure 2, we display two functional classification scenarios. The goal in those scenarios is to distinguish the class type of the curve, which can also be understood as a function-on-scalar problem. Figure 1(a) shows the vertical position of an actor’s hand while either drawing a toy-gun and aiming at a target, or just imitating the motion with the blank hand. This position is measured over time. The two different types of classes of the curves can be distinguished by the color scheme.
Figure 1(b) shows a data set built for distinguishing images of beetles from images of flies based on their outlines. While following the outline, the distance to the center of the object is measured which is then used for classification purposes. The latter data sets are available from Bagnall et al. (2017b).

Figure 1: Scalar-on-function regression: Spectral data for fossil fuels (Fuchs et al., 2015)
(a) Gunpoint
(b) BeetleFly
Figure 2: Excerpts from two time series classification data sets. (a): Gunpoint data (Ratanamahatana and Keogh, 2005), (b): BeetleFly Data (Hills et al., 2014).

The interested reader is referred to Ramsay (2006) and Kokoszka and Reimherr (2017) for more in-depth introductions to this topic.

4 Functional Data Analysis with mlrFDA

Along with the benchmark, we implement the software mlrFDA, which extends the popular machine learning framework mlr for the analysis of functional data. As the implemented functionality is an extension of the mlr package, all of the functionality available in mlr transfers to the newly added methods for functional data analysis. We include a brief overview of the implemented functionality in A.1. A more in-detail overview and tutorial on mlr can be found in the mlr tutorial (Schiffner et al., 2016).

mlr provides a unified framework for machine learning methods in R, currently supporting tasks from

main problem types: (multilabel-)classification, regression, cluster analysis, and survival analysis. For each problem type, many algorithms (called


) are integrated. This yields an extensive set of modeling options with a unified, simple interface. Moreover, advanced techniques such as hyperparameter tuning, preprocessing and feature selection are also part of the package. An additional focus lies on extensibility, allowing the user to integrate their own algorithms, performance measures and preprocessing methods. As

mlrMBO seamlessly integrates into the new software, many different tuning procedures can be readily adapted by the user. Tuning of hyperparameters is usually not integrated in software packages for functional data analysis and thus would require the user to write additional, non-trivial code that handles (nested) resampling, evaluation and optimization methods.

mlrFDA contains several functional data algorithms from several R packages, e.g., fda.usc, refund or FDboost. The algorithms’ functionality, however, remains unchanged, only their user interface is standardized for use with mlr. For detailed insights into the respective algorithms, full documentation is available in the respective packages.

Since our toolbox is built on mlr’s extensible class system, our framework is easily extensible to other methods that have not yet been integrated, and the user can include his or her own methods which do not necessarily need to be available as a packaged implementation. Additionally, mlrFDA inherits mlr’s functionality for performance evaluation and benchmarking, along with extensive and advanced (hyperparameter) tuning. This makes our platform very attractive for evaluating which algorithm fits best to a problem at hand, and even allows for large benchmark studies.

5 Benchmark Experiment

In order to enable a comparison of the different approaches, an extensive benchmark study is conducted. This paper does not aim to replicate or reproduce results obtained by Bagnall et al. (2017a) or Fawaz et al. (2019). Instead we focus on providing a benchmark complementary to previous benchmarks. This is done because the experiments require large amounts of computational resources, and

the added value of an exact replication of the experiments (with open source code) is comparatively small. Nonetheless, we aim for results that can be compared, and thus extend the results obtained by

Bagnall et al. (2017a) by staying close to their setup. The experiments were carried out on a high performance computing cluster, supported by the Leibniz Rechenzentrum Munich. Individual runs were allowed up to GB of RAM and hours run-time for each evaluation. We want to stress that this benchmark compares implementations, which does not always necessarily correspond to the performance of the corresponding theoretical algorithm. Additionally, methods for functional data analysis are traditionally more focused on valid statistical inference and interpretable results, which does not necessarily coincide with high predictive performance.

5.1 Benchmark Setup

Data sets 51 Data sets, see table 7
Algorithms Function (Package)
Machine Learning: - glmnet (glmnet)
- rpart (rpart)
- ksvm (kernlab)
- ranger (ranger)
- xgboost (xgboost)
Functional Data -


- classif.glm (fda.usc)
- (fda.usc)
- classif.kernel(fda.usc)
- FDboost (FDboost)
- fgam (refund)
- knn with dtw (classiFunc)
Feature Extraction + ML - feature extraction: see table 6
- in combination with ML algorithms marked with a .
Measures mean misclassification error, training time
Resampling 20-fold stratified sub-sampling;
class balances and train/test set size as in Bagnall et al. (2017a).
Tuning 100 iterations of Bayesian optimization (3-fold inner CV).
Corresponding hyperparameter-ranges can be obtained
from tables 3 and 5.
Table 1: Benchmark experiment setup

A benchmark experiment is defined by four important characteristics: The data sets algorithms are tested on, the algorithms to be evaluated, the measures used for evaluating predictive performance, and a resampling strategy used for generating train and test splits of the data. A comprehensive overview of the conducted benchmark setup can be obtained from Table 1.

These characteristics are briefly described subsequently before providing and discussing the results. We use a subset of 51 data sets from the popular UCR archive (Bagnall et al., 2017b) in order to enable a comparison of results in Bagnall et al. (2017a) with the additional methods described in this paper. The data sets stem from various application types such as ECG measurements, sensor data, or image outlines, therefore having varying training set sizes or measurement lengths. For more detailed information about the data sets, see Bagnall et al. (2017b).

We selected data using the following criteria: In order to reduce the computational resources we did not run data sets that have multiple versions, exclude data sets with less then 3 examples in each class remove data sets with more than instances or time series longer than

measurements. As some of the classifiers only work with multi-class targets via

1-vs-all classification, we additionally excluded data sets with more then 40 classes. In essence, we benchmark small and medium sized data sets with a moderate amount of different classes.

We add new algorithms and feature extraction methods which can be combined with arbitrary machine learning methods for scalar features (c.f. Table 1). Additionally we test classical machine learning methods, in order to obtain a broader perspective on expected performance if the functional nature of the data is ignored. As we benchmark default settings as well as tuned algorithms, in total different algorithms are evaluated across all data sets. When combining feature extraction and machine learning methods, we fuse the learning algorithm and the preprocessing, thus treating them as a pipeline where data is internally transformed before applying the learner. This allows us to jointly tune the hyperparameters of learning algorithm and preprocessing method. The respective defaults and parameter ranges can be obtained from Table 3 (feature extractors) and Table 5 (learning algorithms). More detailed description of the hyperparameters can be obtained from the respective packages documentation.

In order to generate train/test splits, and thus obtain an unbiased estimate of the algorithm’s performance, we use stratified sub-sampling. We use 20 different train/test splits for each data set in order to reduce variance and report the average. For tuned models, we use use nested cross-validation

(Bischl et al., 2012) to ensure unbiased estimates, where the outer loop is again subsampling with 20 splits, and the inner resampling for tuning is a 3-fold (stratified) cross-validation. All compared 80 algorithms are presented exactly the same index sets for the 20 train-test outer subsampling splits.

Mean misclassification error (MMCE) is chosen as a measure of predictive performance in order to stay consistent with Bagnall et al. (2017a)

. Other measures, such as area under the curve (AUC) require predicted probabilities and do not trivially extend to multi-class settings.

While Bagnall et al. (2017a) tune all algorithms across a carefully handcrafted grid, we use Bayesian optimization (Snoek et al., 2012). In order to stay comparable, we analogously fix the amount of tuning iterations to 100.

We use mlrMBO (Bischl et al., 2017) in order to perform Bayesian optimization of the hyperparameters of the respective algorithm. Additionally, in order to scale the method to a larger amount of data sets and machines, the R-package batchtools (Bischl et al. (2015), Lang et al. (2017)) is used. This enables running benchmark experiments on high-performance clusters. For the benchmark experiment, a job is defined as re-sampling of a single algorithm (or tuning thereof) on a single version of a data set. This allows for parallelization to an arbitrary number of CPU’s, while at the same time guaranteeing reproducibility. The code for the benchmark is available from for reproducibility.

5.2 Results

This Section tries to answer the questions posed in section 1. We evaluate various machine learning algorithms in combination with feature extraction, classical time series classification approaches, the effect of tuning hyperparameters for several methods, and try to give recommendations with respect to which algorithm(s) to choose for new classification problems.

Algorithms evaluated in this benchmark have been divided into three groups: Algorithms specifically tailored to functional data, classical machine learning algorithms without feature extraction and classical machine learning algorithms in combination with feature extraction.

5.2.1 Algorithms for functional data

Performances of algorithms specifically tailored to functional data analysis can be obtained from Figure 3. The k-nearest neighbors algorithm from package classiFunc (Maierhofer and Pfisterer, 2018) in combination with dynamic time warping (Rakthanmanon et al., 2012) distance seems to perform best across data sets. It is also considered a strong baseline in Bagnall et al. (2017a).

Figure 3: Performances for functional data analysis algorithms in default settings (untuned) across all data sets.

5.2.2 Machine Learning algorithms with feature extraction

Performances of different machine learning algorithms in combination with feature extraction with and without tuning can be obtained from Figure 4.

Figure 4: Results for feature extraction-based machine learning algorithms with default and tuned (MBO) hyperparameters across data sets. Hyperparameters are tuned jointly for learner and feature extraction method.

We conclude that feature extraction using splines (bsignal) and wavelets as well as extracting dynamic time warping distances works well when combined with conventional machine learning algorithms, even at their default hyper-parameters. Among the learners, random forests, especially in combination with bsignal show quite advantageous performance. In addition, we find an obvious improvement from hyper-parameter tuning for the Fourier feature extraction. In terms of learners, random forest and gradient boosted tree learners (xgboost) perform better than support vector machines.

5.2.3 Machine Learning algorithms without feature extraction

Additionally, we aim to provide some insight with regards to the performance of machine learning algorithms that ignore the functional nature of our data. Figure 5 provides an overview over the performance of different machine learning algorithms that are often used together with traditional tabular data. Performances in this figure are obtained from algorithms directly applied to the functional data without any additional feature extraction. The widely used gradient boosting (xgboost) and random forest (ranger) implementations seem to work reasonably well for functional data even without additional feature extraction.

Figure 5: Performance of non-functional machine learning algorithms across data sets applied directly to functional data with and without tuning.

5.2.4 The effect of tuning hyperparameters

From our experiments, we conclude, that tuning hyperparameters of machine learning algorithms in general has a non-negligible effect on the performance. Using Bayesian optimization in order to tune algorithm hyperparameters on average yielded an absolute increase in accuracy of 5.4% across data sets and learners.

Figure 6 displays the aggregated time over all data sets, taking into account the time required for hyperparameter tuning. All experiments have been run on equivalent hardware on high-performance computing infrastructure. Due to fluctuations in server load, this does not allow for an exact comparison with respect to computation time, but we hope to achieve comparable results as we repeatedly evaluate on sub-samples. Note that we restrict the tuning to algorithms where tuning traditionally leads to higher performances.333Additionally, we find significantly improved performance for tuned FDboost in Figure 3

Figure 6: Comparison of running time for the different learner classes with default and tuned hyperparameters across data sets. A log transformation on the running time in seconds is applied, and the mean running time is visualized for each stratification as a horizontal line within the violin plot.

5.2.5 Top 10 Algorithms and recommendations

Table 2 showcases the top algorithms from the benchmark in terms of average rank in predictive accuracy across data sets. With this list, we aim to provide some initial understanding of the performance of different algorithms and feature extraction methods. Note that this list by no means reflects performance on future data sets, but might serve as an indicator, of which algorithms one might want to try first given computational constraints.

Algorithm Setting Accuracy % Average Rank
ranger_wavelet_tuned 0.92 12.90
xgboost_wavelet_tuned 0.92 14.45
ranger_bsignal_tuned 0.90 15.02
knn_dtw_tuned 0.92 15.22
ranger_none_default 0.90 15.59
ranger_bsignal_default 0.89 15.71
ranger_wavelet_default 0.90 16.33
knn_dtw_default 0.92 16.43
xgboost_bsignal_tuned 0.90 17.57
ranger_none_tuned 0.89 18.49
Table 2: Top 10 algorithms by average rank across all data sets. Percent Accuracy describes the fraction of the maximal accuracy reached for each task.

We observe that wavelet extraction in combination with either ranger or xgboost seems to be very strong. They obtain an average rank of 12.90 and 14.45 (out of 80) respectively. Dynamic time warping distances for k-nearest neighbors indeed seems to be a strong baseline, even without tuning. Another strong feature extraction method seems to be the extraction of B-spline features. Using the algorithms above allows us to obtain an accuracy within of the maximum on of the data sets.

If the only criterion for model selection is predictive performance, (tuned) machine learning models in combination with feature extraction is a competitive baseline. This class of methods achieves within 95% of the optimal performance on out of data sets, while they include the best performing classifier in cases.

id type values def. trafo
bsignal.knots int {3,…,500} 10 -
bsignal.df int {1,…,10} 3 -
res.level int {2,…,5} - -
shift num [0.01,1] - -
rank. int {1,…,30} - -
filter chr d4,d8,d20,la8,la20,bl14,bl20,c6,c24 - -
boundary chr periodic,reflection - -
trafo.coeff chr phase,amplitude - -
ref.method chr random,all random -
n.refs num [0,1] - -
dtwwindow num [0,1] - -
Table 3: Parameter spaces and default settings for feature extraction methods.

5.2.6 Comparison to classical time series classification

Even though the main purpose of this paper is not a direct comparison with the results from Bagnall et al. (2017a), we can use our results to show that applying functional data approaches and classical machine learning approaches together with feature extraction can still improve classification accuracy compared to current state-of-the-art time series classification methods.
In the experiments we conducted, the methods described in this paper improved accuracy on 9 out of the 51 data sets which is displayed in Figure 7. The 9 data sets and the corresponding best learner are displayed in Table 4. For each data set, only the best reached accuracy for both sets of algorithms is displayed.

Name Algorithm_Setting Accuracy
Beef xgboost_wavelet_tuned 0.83
ChlorineConcentration ksvm_none_tuned 0.91
DistalPhalanxOutlineAgeGroup ranger_none_default 0.83
DistalPhalanxOutlineCorrect ranger_dtwkernel_default 0.83
DistalPhalanxTW ranger_bsignal_default 0.76
Earthquakes FDboost_none_default 0.80
Ham xgboost_wavelet_tuned 0.84
InsectWingbeatSound ranger_wavelet_default 0.65
SonyAIBORobotSurface1 ksvm_wavelet_default 0.94
Table 4: Data sets together with corresponding mlrFDA learner and accuracy for which our learners were able to improve accuracy in the conducted experiments.
Figure 7: Comparing accuracy between our mlrFDA learners and the classical time series classification algorithms in Bagnall et al. (2017a). For each data set, only the best accuracy for each of the two benchmarks is shown. We observe that for 9 of the evaluated data sets the classification performance can directly be improved solely by applying our mlrFDA learners, while we perform on par with the classical time series classification algorithms (when rounding to 3 decimal digits) on two data sets.

Additionally, we evaluate how our learners rank in comparison to the individual bake-off algorithms from Bagnall et al. (2017a). The algorithm which performs best on a data set obtains the rank 1. The mean rank of the individual learners over all data sets (we take the intersection of the data sets from our benchmark and the ones from Bagnall et al. (2017a)). The average sorted ranks for the top 50% algorithms are displayed in Figure  8. We observe that the ensemble methods get the top ranks, which is no surprise, as for instance the COTE algorithm Bagnall et al. (2015) internally combines several classifiers from 4 different time series domains.

However, compared to the classical time series algorithms from Bagnall et al. (2017a) with the ensemble methods removed, our functional data algorithms obtain an overall good rank in accuracy performance, interleaved with the algorithms from Bagnall et al. (2017a). Note that the benchmarks are not exactly comparable due to minor differences in the benchmark setup, and we instead only include their reported results.

Figure 8: Comparing sorted average performance ranks between our mlrFDA learners (algorithm names in lower case) and the classical time series classification algorithms (algorithm names in capital) in Bagnall et al. (2017a), The mean rank of each individual learner over all data sets is displayed. Only the first half of all algorithms being compared are displayed here. We observe that the Ensemble Methods like HIVE.COTE, FLAT.COTE, ST, BOSS, EE occupy the top tier, while the rest of the rank space are interleaved by our mlrFDA algorithms and algorithms from Bagnall et al. (2017a)
parameter type values default trafo
C num [-15,15] - 2^x
sigma num [-15,10] - 2^x
mtry.power num [0,1] -
min.node.size num [0,0.99] -
sample.fraction num [0.1,1] - -
nrounds int {1,…,5000} 100 -
eta num [-10,0] - 2^x
subsample num [0.1,1] - -
booster chr gbtree,gblinear - -
max_depth int {1,…,15} - -
min_child_weight num [0,7] - 2^x
colsample_bytree num [0,1] - -
colsample_bylevel num [0,1] - -
lambda num [-10,10] - 2^x
alpha num [-10,10] - 2^x
mstop int {1,…,5000} 100 -
nu num [0,1] 0.01 -
df num [1,5] 4 -
knots int {5,…,100} 10 -
degree int {1,…,4} 3 -
Table 5: Parameter spaces and defaults used for tuning machine learning and functional data algorithms. In case no default is provided, package defaults are used. Additional information can be found in the respective packages documentation.

6 Summary and Outlook

In this work, we provide a benchmark along with a software implementation that integrates the functionality of a diverse set of R-packages into a single user interface and API. Both contributions come with a multiplicity of benefits:

  • The user is not required to learn and deal with the vast complexity of the different interfaces the underlying packages expose.

  • All of the existing functionality (e.g., preprocessing, resampling, performance measures, tuning, parallelization) of the mlr ecosystem can now be used in conjunction with already existing algorithms for functional data.

  • We expose functionality that allows us to work with functional data using traditional machine learning methods via feature extraction methods.

  • Integration of additional preprocessing methods or models is (fairly) trivial and automatically benefits from the full mlr ecosystem.

In order to obtain a broader overview of the performance of the integrated methods, we perform a large benchmark study. This allows users to get an initial overview of potential performances of the different algorithms. Specifically,

  • We open up new perspectives for time series classification tasks by incorporating methods from functional data analysis, as well as feature transformations combined with conventional machine learning models.

  • Based on the large scale benchmark, we conclude that many learners have competitive performance (Figure 7) and additionally offer the interpretability of many functional data analysis methods. Our toolbox serves as a strong complement and alternative to other time series classification software.

  • The presented benchmark study uses state-of-the-art Bayesian optimization for hyperparameter optimization, which results in significant improvements over models that are not tuned. This kind of hyperparameter tuning is easy to do with mlrFDA. Tuning, albeit heavily influencing performance is often not investigated. Our benchmark closes this gap in existing literature.

  • We find that extracting vector valued features and feeding them to a conventional machine learning model can often form competitive learners.

  • The pareto-optimal set in terms of performance on each data set contains different algorithmfeature-extraction combinations. Our toolbox offers the same API for all methods and allows to automatically search over this space, and thus allows users to obtain optimal models without knowing all underlying methods.

Concerning the questions we proposed at the beginning of the paper, we draw the following conclusions:

  • Tuning only a subset of the presented learners and feature extractions, i.e., the methods listed in Table 2, is sufficient to achieve good performances on almost all data sets in our benchmark.

  • A simple random forest without any preprocessing can also be a reasonable baseline for time series data. It achieves an average rank of (top 4) in our benchmark.

  • Most algorithms for functional data (e.g., FDboost) do not perform well in our benchmark study. As those algorithms are fully interpretable and offer statistically valid coefficients, they can still be useful in some applications, and should thus not be ruled out.

  • Feature extraction techniques, such as b-spline representations (bsignal) and wavelet extraction work well in conjunction with machine learning techniques for vector valued features such as xgboost and random forest.

  • Tuning leads to an average reduction in absolute MMCE of (ranger), (xgboost), (ksvm) (across feature extraction techniques) and (FDboost). This holds for all feature extraction techniques, where improvements range from multires to fourier.

In future work we will continue to expand the available toolbox along with a benchmark of new methods, and provide the R community a wider range of methods that can be used for the analysis of functional data. This includes not only integrating many already available packages, and as a result to enable preprocessing operations such as smoothing (e.g., fda (Ramsay et al., 2018)) and alignment (e.g., fdasrvf (Tucker, 2016) or tidyfun (Scheipl and Goldsmith, 2019)

), but also to explore and integrate advanced imputation methods for functional data. Further work will also extend the current implementation to support data that is measured on unequal or irregular grids. Additionally, we aim to implement some of the current state-of-the art machine learning models from the time series classification bake-off

(Bagnall et al., 2017a), such as the Collective of Transformation-Based Ensembles (COTE) (Bagnall et al., 2015). This enables researchers to use and compare with current state-of-the-art methods.


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Appendix A API overview

For the interested reader, we introduce a brief overview of the API and functionality.

a.1 Representing functional data in mlrFDA

A sketch of the data structure we use to represent functional data can be found in the right part of Figure 9. We assume a data set consists of data for observational units, organized in rows of features, where one row contains all observed features for one observational unit, i.e., each row typically contains several functional and/or scalar covariates. For a classical, non-functional data set, the features are single columns (as depicted in the left part of Figure 9). A functional data set, on the other hand, consists of single-column scalar features as well as functional features of different length for each functional covariate, each represented by multiple adjacent and connected columns. Each of these columns contains the evaluations of the functional feature at a certain argument value for all observational units (right part of Figure 9).

Figure 9: Schematic comparison of non-functional and functional data representation in mlrFDA. The green feature is a functional feature spanning multiple columns.

As an example which will be used throughout the remainder of this paper, we use the fuelSubset data set from package FDboost, see also Figure 1. It contains a numeric target variable heatan, the fuel’s heating value, a scalar feature h20, the fuel’s water content, and two functional features NIR and UVVIS, measured at 231 and 129 wavelengths, respectively. To start with a clean sheet, we create a data.frame containing all features as separate columns.

R¿ library(mlr) R¿ library(FDboost) R¿ df = data.frame(fuelSubset[c(”heatan”, ”h2o”, ”UVVIS”, ”NIR”)])

The first step when setting up an experiment in any analysis is to make the data accessible for the specific algorithms that will be applied. In mlr, the data itself, and additional information, such as which column corresponds to the target variable is stored as a Task, requiring the input data to be of type data.frame.

The list of column positions of the functional features is then passed as argument fd.features to makeFunctionalData(), which returns an object of type data.frame in which the columns corresponding to each functional feature are combined into matrix columns. 444As an alternative, a list of the column names containing the functional features is also valid as argument to fd.features, which is especially useful if columns are already labeled.

R¿ fd.features = list(”UVVIS” = 3:136, ”NIR” = 137:367) R¿ fdf = makeFunctionalData(df, fd.features = fd.features) R¿ str(fdf) ’data.frame’: 129 obs. of 4 variables: $ heatan: num 26.8 27.5 23.8 18.2 17.5 … $ h2o : num 2.3 3 2 1.85 2.39 … $ UVVIS : num [1:129, 1:134] 0.145 -1.584 -0.814 -1.311 -1.373 … ..- attr(*, ”dimnames”)=List of 2 .. ..$ : NULL .. ..$ : chr ”UVVIS.1” ”UVVIS.2” ”UVVIS.3” ”UVVIS.4” … $ NIR : num [1:129, 1:231] 0.2818 0.2916 -0.0042 -0.034 -0.1804 … ..- attr(*, ”dimnames”)=List of 2 .. ..$ : NULL .. ..$ : chr ”NIR.1” ”NIR.2” ”NIR.3” ”NIR.4” …

We additionally specify the name "fuelsubset" and the target variable "heatan". The structure of the functional Task object is rather similar to the non-functional Task, with the additional information functionals, which states how many functional features are present in the underlying data.

R¿ tsk1 = makeRegrTask(”fuelsubset”, data = fdf, target = ”heatan”) R¿ print(tsk1) Supervised task: fuelsubset Type: regr Target: heatan Observations: 129 Features: numerics factors ordered functionals 1 0 0 2 Missings: FALSE Has weights: FALSE Has blocking: FALSE Has coordinates: FALSE

After defining the task, a learner is created by calling makeLearner. This contains the algorithm that will be fitted on the data in order to obtain a model. Currently, mlrFDA supports both functional regression and functional classification. A list of supported learners can be found in Table 1

a.2 Machine Learning and Feature Extraction

Classical machine learning algorithms do not take into account the characteristics of functional data and treat the input data as vector valued features. Without additional preprocessing, this typically yields poor performance on, as the models cannot exploit the lower intrinsic dimensionality of the functional covariates nor the fact that they represent observations over a continuum.

In mlrFDA, classical algorithms can be applied to functional data, however, a warning message will be displayed. In our example, we train a partitioning tree on the functional data, while ignoring the functional structure.

R¿ rpart.lrn = makeLearner(”regr.rpart”) R¿ m = train(learner = rpart.lrn, task = tsk1) Functional features have been converted to numerics

For conventional learning algorithms to work well on functional data, informative scalar features need to be extracted from the functional features.

Feature extraction is applied in practice for a multiplicity of reasons, as it often not only reduces the dimensionality of the resulting problem, but also allows researchers to make use of domain knowledge, for example by hand-crafting features from measurements of continuous processes. Examples for this include deriving features that allow for sleepiness detection (Jin et al., 2013), or by extracting features from electro-cardiogram data in order to detect emotions (Murugappan et al., 2008). The resulting features often have a much lower dimensionality, which often improves fitted models. Other preprocessing methods for functional or time series data include extracting general purpose features such as wavelet coefficients (Aldrich, 2013; Soltani, 2002), principal component scores or Fourier coefficients. The resulting scalar features can then be used with different machine learning methods such as -nearest neighbors.

In the following section, we showcase the feature extraction procedure using general purpose features as an example. We want to emphasize that it is also easily possible to write custom feature extraction methods using the makeFeatureExtractionMethod function.

Name Function Package
Discrete Wavelet Transform extractFDAWavelets() wavelets
Fast Fourier Transform extractFDAFourier() stats
Principal Component Analysis extractFDAPCA() stats
B-Spline Features extractFDABsignal() FDboost
Multi-Resolution Feature Extraction extractFDAMultiResFeatures() -
Time Series Features extractFDATsfeatures() tsfeatures
Dynamic Time-Warping Kernel extractFDADTWKernel() rucrdtw
Table 6: Feature extraction methods currently implemented in mlrFDA and underlying packages

In our example, we extract the Fourier coefficients from the functional feature UVVIS, and principal component scores from the second functional feature NIR in order to transform the original task with functional data into a conventional task.

R¿ feat.methods = list(”UVVIS” = extractFDAFourier(), ”NIR” = extractFDAPCA()) R¿ extracted = extractFDAFeatures(tsk, feat.methods = feat.methods) R¿ extracted

desc Extraction of features from functional data: Target: heatan Functional Features: 2; Extracted features: 2

As an alternative, the feature extraction can be applied in a wrapper method
makeExtractFDAFeatsWrapper(). In general, a wrapper combines a learner method with another method, thereby creating a new learner that can be handled like any other learner. In our case, a classical machine learning method is combined with the data preprocessing step of feature transformation from functional to non-functional data.

R¿ wrapped.lrn = makeExtractFDAFeatsWrapper(”regr.rpart”, feat.methods = feat.methods)

This is suitable for honest cross-validation of data-adaptive feature extraction methods like principal components. We can now cross-validate the learner created above using mlr’s resample function with 10-fold cross-validation.

R¿ res = resample(learner = wrapped.lrn, task = tsk1, resampling = cv10)

In the same way, we can train and predict on data, or benchmark multiple learners across multiple data sets. Additionally, we can apply a tuneWrapper to our learner in order to automatically tune hyperparameters of the learner and the preprocessing method during cross-validation.

Appendix B Data sets used in the Benchmark

Table 7 contains all data sets used in the benchmark along with additional data properties.

Name Obs. Classes Length Type Split
Adiac 781 37 176 IMAGE 0.50
ArrowHead 211 3 251 IMAGE 0.17
Beef 60 5 470 SPECTRO 0.50
BeetleFly 40 2 512 IMAGE 0.50
BirdChicken 40 2 512 IMAGE 0.50
Car 120 4 577 SENSOR 0.50
CBF 930 3 128 SIMULATED 0.03
ChlorineConcentration 4307 3 166 SIMULATED 0.11
Coffee 56 2 286 SPECTRO 0.50
Computers 500 2 720 DEVICE 0.50
CricketX 780 12 300 MOTION 0.50
DistalPhalanxOutlineAgeGroup 539 3 80 IMAGE 0.74
DistalPhalanxOutlineCorrect 876 2 80 IMAGE 0.68
DistalPhalanxTW 539 6 80 IMAGE 0.74
Earthquakes 461 2 512 SENSOR 0.70
ECG200 200 2 96 ECG 0.50
ECGFiveDays 884 2 136 ECG 0.03
ElectricDeviceOn 1008 2 360 DEVICE 0.63
EpilepsyX 275 4 208 HAR 0.61
FaceAll 2250 14 131 IMAGE 0.25
FacesUCR 2250 14 131 IMAGE 0.09
Fish 350 7 463 IMAGE 0.50
GunPoint 200 2 150 MOTION 0.25
Ham 214 2 431 SPECTRO 0.51
Herring 128 2 512 IMAGE 0.50
InsectWingbeatSound 2200 11 256 SENSOR 0.10
ItalyPowerDemand 1096 2 24 SENSOR 0.06
LargeKitchenAppliances 750 3 720 DEVICE 0.50
Lightning2 121 2 637 SENSOR 0.50
Lightning7 143 7 319 SENSOR 0.49
Meat 120 3 448 SPECTRO 0.50
MedicalImages 1141 10 99 IMAGE 0.33
MoteStrain 1272 2 84 SENSOR 0.02
OSULeaf 442 6 427 IMAGE 0.45
Plane 210 7 144 SENSOR 0.50
RefrigerationDevices 750 3 720 DEVICE 0.50
ScreenType 750 3 720 DEVICE 0.50
ShapeletSim 200 2 500 SIMULATED 0.10
SmallKitchenAppliances 750 3 720 DEVICE 0.50
SonyAIBORobotSurface1 621 2 70 SENSOR 0.03
Strawberry 983 2 235 SPECTRO 0.62
SwedishLeaf 1125 15 128 IMAGE 0.44
SyntheticControl 600 6 60 SIMULATED 0.50
ToeSegmentation1 268 2 277 MOTION 0.15
Trace 200 4 275 SENSOR 0.50
TwoLeadECG 1162 2 82 ECG 0.02
TwoPatterns 5000 4 128 SIMULATED 0.20
UWaveGestureLibraryX 4478 8 315 MOTION 0.20
Wafer 7164 2 152 SENSOR 0.14
Wine 111 2 234 SPECTRO 0.51
Yoga 3300 2 426 IMAGE 0.09
Table 7: Data sets from the UCI Archive used in the benchmark.

Appendix C Failed and missing experiments

Experiments for some algorithm / data set combinations failed due to implementation details or algorithm properties. In order to increase transparency, failed algorithms are listed here, and if available reasons for failure are provided.

At the time of the benchmark, the implementation in the tsfeatures package was not stable enough to be included in the benchmark.

  • classif.fgam
    Data sets: BeetleFly, BirdChicken, Coffee, Computers, DistalPhalanxOutlineCorrect, Earthquakes, ECG200, ECGFiveDays, ElectricDeviceOn, GunPoint, Ham, Herring, ItalyPowerDemand, Lightning2, MoteStrain, ShapeletSim, SonyAIBORobotSurface1, Strawberry, ToeSegmentation1, TwoLeadECG, Wafer, Wine, Yoga
    Reason: Too few instances in some classes, such that p ¿ n.

  • classif.fdausc.kernel and .np Data sets: ElectricDeviceOn, ShapeletSim

  • classif.fdausc.knn
    Data sets: DistalPhalanxTW, EpilepsyX