4,959 research outputs found
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
FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences
We study the problem of classifying interval-based temporal sequences
(IBTSs). Since common classification algorithms cannot be directly applied to
IBTSs, the main challenge is to define a set of features that effectively
represents the data such that classifiers can be applied. Most prior work
utilizes frequent pattern mining to define a feature set based on discovered
patterns. However, frequent pattern mining is computationally expensive and
often discovers many irrelevant patterns. To address this shortcoming, we
propose the FIBS framework for classifying IBTSs. FIBS extracts features
relevant to classification from IBTSs based on relative frequency and temporal
relations. To avoid selecting irrelevant features, a filter-based selection
strategy is incorporated into FIBS. Our empirical evaluation on eight
real-world datasets demonstrates the effectiveness of our methods in practice.
The results provide evidence that FIBS effectively represents IBTSs for
classification algorithms, which contributes to similar or significantly better
accuracy compared to state-of-the-art competitors. It also suggests that the
feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer,
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