27 research outputs found
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Time Series Classification (TSC) problems are encountered in many real life
data mining tasks ranging from medicine and security to human activity
recognition and food safety. With the recent success of deep neural networks in
various domains such as computer vision and natural language processing,
researchers started adopting these techniques for solving time series data
mining problems. However, to the best of our knowledge, no previous work has
considered the vulnerability of deep learning models to adversarial time series
examples, which could potentially make them unreliable in situations where the
decision taken by the classifier is crucial such as in medicine and security.
For computer vision problems, such attacks have been shown to be very easy to
perform by altering the image and adding an imperceptible amount of noise to
trick the network into wrongly classifying the input image. Following this line
of work, we propose to leverage existing adversarial attack mechanisms to add a
special noise to the input time series in order to decrease the network's
confidence when classifying instances at test time. Our results reveal that
current state-of-the-art deep learning time series classifiers are vulnerable
to adversarial attacks which can have major consequences in multiple domains
such as food safety and quality assurance.Comment: Accepted at IJCNN 201
Transfer learning for time series classification
Transfer learning for deep neural networks is the process of first training a
base network on a source dataset, and then transferring the learned features
(the network's weights) to a second network to be trained on a target dataset.
This idea has been shown to improve deep neural network's generalization
capabilities in many computer vision tasks such as image recognition and object
localization. Apart from these applications, deep Convolutional Neural Networks
(CNNs) have also recently gained popularity in the Time Series Classification
(TSC) community. However, unlike for image recognition problems, transfer
learning techniques have not yet been investigated thoroughly for the TSC task.
This is surprising as the accuracy of deep learning models for TSC could
potentially be improved if the model is fine-tuned from a pre-trained neural
network instead of training it from scratch. In this paper, we fill this gap by
investigating how to transfer deep CNNs for the TSC task. To evaluate the
potential of transfer learning, we performed extensive experiments using the
UCR archive which is the largest publicly available TSC benchmark containing 85
datasets. For each dataset in the archive, we pre-trained a model and then
fine-tuned it on the other datasets resulting in 7140 different deep neural
networks. These experiments revealed that transfer learning can improve or
degrade the model's predictions depending on the dataset used for transfer.
Therefore, in an effort to predict the best source dataset for a given target
dataset, we propose a new method relying on Dynamic Time Warping to measure
inter-datasets similarities. We describe how our method can guide the transfer
to choose the best source dataset leading to an improvement in accuracy on 71
out of 85 datasets.Comment: Accepted at IEEE International Conference on Big Data 201
Fractal interpolation in the context of prediction accuracy optimization
This paper focuses on the hypothesis of optimizing time series predictions
using fractal interpolation techniques. In general, the accuracy of machine
learning model predictions is closely related to the quality and quantitative
aspects of the data used, following the principle of \textit{garbage-in,
garbage-out}. In order to quantitatively and qualitatively augment datasets,
one of the most prevalent concerns of data scientists is to generate synthetic
data, which should follow as closely as possible the actual pattern of the
original data.
This study proposes three different data augmentation strategies based on
fractal interpolation, namely the \textit{Closest Hurst Strategy},
\textit{Closest Values Strategy} and \textit{Formula Strategy}. To validate the
strategies, we used four public datasets from the literature, as well as a
private dataset obtained from meteorological records in the city of Brasov,
Romania. The prediction results obtained with the LSTM model using the
presented interpolation strategies showed a significant accuracy improvement
compared to the raw datasets, thus providing a possible answer to practical
problems in the field of remote sensing and sensor sensitivity. Moreover, our
methodologies answer some optimization-related open questions for the fractal
interpolation step using \textit{Optuna} framework
Instance-based Counterfactual Explanations for Time Series Classification
In recent years, there has been a rapidly expanding focus on explaining the
predictions made by black-box AI systems that handle image and tabular data.
However, considerably less attention has been paid to explaining the
predictions of opaque AI systems handling time series data. In this paper, we
advance a novel model-agnostic, case-based technique -- Native Guide -- that
generates counterfactual explanations for time series classifiers. Given a
query time series, , for which a black-box classification system
predicts class, , a counterfactual time series explanation shows how
could change, such that the system predicts an alternative class, . The
proposed instance-based technique adapts existing counterfactual instances in
the case-base by highlighting and modifying discriminative areas of the time
series that underlie the classification. Quantitative and qualitative results
from two comparative experiments indicate that Native Guide generates
plausible, proximal, sparse and diverse explanations that are better than those
produced by key benchmark counterfactual methods
Neural Data Augmentation Techniques for Time Series Data and its Benefits
Exploring adversarial attacks and studying their effects on machine learning algorithms has been of interest to researchers. Deep neural networks working with time series data have received lesser interest compared to their image counterparts in this context. In a recent finding, it has been revealed that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks. In this paper, we introduce neural data augmentation techniques and show that classifier trained with such augmented data obtains state-of-the-art classification accuracy as well as adversarial accuracy against Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) on various time series benchmarks. © 2020 IEEE
Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey
Data Augmentation (DA) has emerged as an indispensable strategy in Time
Series Classification (TSC), primarily due to its capacity to amplify training
samples, thereby bolstering model robustness, diversifying datasets, and
curtailing overfitting. However, the current landscape of DA in TSC is plagued
with fragmented literature reviews, nebulous methodological taxonomies,
inadequate evaluative measures, and a dearth of accessible, user-oriented
tools. In light of these challenges, this study embarks on an exhaustive
dissection of DA methodologies within the TSC realm. Our initial approach
involved an extensive literature review spanning a decade, revealing that
contemporary surveys scarcely capture the breadth of advancements in DA for
TSC, prompting us to meticulously analyze over 100 scholarly articles to
distill more than 60 unique DA techniques. This rigorous analysis precipitated
the formulation of a novel taxonomy, purpose-built for the intricacies of DA in
TSC, categorizing techniques into five principal echelons:
Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and
Automated Data Augmentation. Our taxonomy promises to serve as a robust
navigational aid for scholars, offering clarity and direction in method
selection. Addressing the conspicuous absence of holistic evaluations for
prevalent DA techniques, we executed an all-encompassing empirical assessment,
wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR
time-series datasets, employing ResNet and a multi-faceted evaluation paradigm
encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a
benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the
inconsistent efficacies of DA techniques, with..
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