7 research outputs found
Adaptively constrained dynamic time warping for time series classification and clustering
Time series classification and clustering are important for data mining research, which is conducive to recognizing movement patterns, finding customary routes, and detecting abnormal trajectories in transport (e.g. road and maritime) traffic. The dynamic time warping (DTW) algorithm is a classical distance measurement method for time series analysis. However, the over-stretching and over-compression problems are typical drawbacks of using DTW to measure distances. To address these drawbacks, an adaptive constrained DTW (ACDTW) algorithm is developed to calculate the distances between trajectories more accurately by introducing new adaptive penalty functions. Two different penalties are proposed to effectively and automatically adapt to the situations in which multiple points in one time series correspond to a single point in another time series. The novel ACDTW algorithm can adaptively adjust the correspondence between two trajectories and obtain greater accuracy between different trajectories. Numerous experiments on classification and clustering are undertaken using the UCR time series archive and real vessel trajectories. The classification results demonstrate that the ACDTW algorithm performs better than four state-of-the-art algorithms on the UCR time series archive. Furthermore, the clustering results reveal that the ACDTW algorithm has the best performance among three existing algorithms in modeling maritime traffic vessel trajectory. © 2020 Elsevier Inc
Making the Dynamic Time Warping Distance Warping-Invariant
The literature postulates that the dynamic time warping (dtw) distance can
cope with temporal variations but stores and processes time series in a form as
if the dtw-distance cannot cope with such variations. To address this
inconsistency, we first show that the dtw-distance is not warping-invariant.
The lack of warping-invariance contributes to the inconsistency mentioned above
and to a strange behavior. To eliminate these peculiarities, we convert the
dtw-distance to a warping-invariant semi-metric, called time-warp-invariant
(twi) distance. Empirical results suggest that the error rates of the twi and
dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense.
However, the twi-distance requires less storage and computation time than the
dtw-distance for a broad range of problems. These results challenge the current
practice of applying the dtw-distance in nearest-neighbor classification and
suggest the proposed twi-distance as a more efficient and consistent option.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0996
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
Optimizing Dynamic Time Warping’s Window Width for Time Series Data Mining Applications
Dynamic Time Warping (DTW) is a highly competitive distance measure for most time series data mining problems. Obtaining the best performance from DTW requires setting its only parameter, the maximum amount of warping (w). In the supervised case with ample data, w is typically set by cross-validation in the training stage. However, this method is likely to yield suboptimal results for small training sets. For the unsupervised case, learning via cross-validation is not possible because we do not have access to labeled data. Many practitioners have thus resorted to assuming that “the larger the better”, and they use the largest value of w permitted by the computational resources. However, as we will show, in most circumstances, this is a naïve approach that produces inferior clusterings. Moreover, the best warping window width is generally non-transferable between the two tasks, i.e., for a single dataset, practitioners cannot simply apply the best w learned for classification on clustering or vice versa. In addition, we will demonstrate that the appropriate amount of warping not only depends on the data structure, but also on the dataset size. Thus, even if a practitioner knows the best setting for a given dataset, they will likely be at a lost if they apply that setting on a bigger size version of that data. All these issues seem largely unknown or at least unappreciated in the community. In this work, we demonstrate the importance of setting DTW’s warping window width correctly, and we also propose novel methods to learn this parameter in both supervised and unsupervised settings. The algorithms we propose to learn w can produce significant improvements in classification accuracy and clustering quality. We demonstrate the correctness of our novel observations and the utility of our ideas by testing them with more than one hundred publicly available datasets. Our forceful results allow us to make a perhaps unexpected claim; an underappreciated “low hanging fruit” in optimizing DTW’s performance can produce improvements that make it an even stronger baseline, closing most or all the improvement gap of the more sophisticated methods proposed in recent years
Deep learning for time series classification
Time series analysis is a field of data science which is interested in
analyzing sequences of numerical values ordered in time. Time series are
particularly interesting because they allow us to visualize and understand the
evolution of a process over time. Their analysis can reveal trends,
relationships and similarities across the data. There exists numerous fields
containing data in the form of time series: health care (electrocardiogram,
blood sugar, etc.), activity recognition, remote sensing, finance (stock market
price), industry (sensors), etc. Time series classification consists of
constructing algorithms dedicated to automatically label time series data. The
sequential aspect of time series data requires the development of algorithms
that are able to harness this temporal property, thus making the existing
off-the-shelf machine learning models for traditional tabular data suboptimal
for solving the underlying task. In this context, deep learning has emerged in
recent years as one of the most effective methods for tackling the supervised
classification task, particularly in the field of computer vision. The main
objective of this thesis was to study and develop deep neural networks
specifically constructed for the classification of time series data. We thus
carried out the first large scale experimental study allowing us to compare the
existing deep methods and to position them compared other non-deep learning
based state-of-the-art methods. Subsequently, we made numerous contributions in
this area, notably in the context of transfer learning, data augmentation,
ensembling and adversarial attacks. Finally, we have also proposed a novel
architecture, based on the famous Inception network (Google), which ranks among
the most efficient to date.Comment: PhD thesi