692 research outputs found
Highly comparative feature-based time-series classification
A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time series in terms of their correlation structure, distribution, entropy,
stationarity, scaling properties, and fits to a range of time-series models.
After computing thousands of features for each time series in a training set,
those that are most informative of the class structure are selected using
greedy forward feature selection with a linear classifier. The resulting
feature-based classifiers automatically learn the differences between classes
using a reduced number of time-series properties, and circumvent the need to
calculate distances between time series. Representing time series in this way
results in orders of magnitude of dimensionality reduction, allowing the method
to perform well on very large datasets containing long time series or time
series of different lengths. For many of the datasets studied, classification
performance exceeded that of conventional instance-based classifiers, including
one nearest neighbor classifiers using Euclidean distances and dynamic time
warping and, most importantly, the features selected provide an understanding
of the properties of the dataset, insight that can guide further scientific
investigation
Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining
The chapter is organized as follows. Section 2 will introduce the similarity matching
problem on time series. We will note the importance of the use of efficient data structures to
perform search, and the choice of an adequate distance measure. Section 3 will show some
of the most used distance measure for time series data mining. Section 4 will review the
above mentioned dimensionality reduction techniques
A Review of Subsequence Time Series Clustering
Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies
Mining time-series data using discriminative subsequences
Time-series data is abundant, and must be analysed to extract usable knowledge. Local-shape-based methods offer improved performance for many problems, and a
comprehensible method of understanding both data and models.
For time-series classification, we transform the data into a local-shape space using a shapelet transform. A shapelet is a time-series subsequence that is discriminative
of the class of the original series. We use a heterogeneous ensemble classifier on the transformed data. The accuracy of our method is significantly better than the time-series classification benchmark (1-nearest-neighbour with dynamic time-warping distance), and significantly better than the previous best shapelet-based classifiers.
We use two methods to increase interpretability: First, we cluster the shapelets using a novel, parameterless clustering method based on Minimum Description Length,
reducing dimensionality and removing duplicate shapelets. Second, we transform the shapelet data into binary data reflecting the presence or absence of particular
shapelets, a representation that is straightforward to interpret and understand.
We supplement the ensemble classifier with partial classifocation. We generate rule sets on the binary-shapelet data, improving performance on certain classes, and revealing the relationship between the shapelets and the class label. To aid interpretability, we use a novel algorithm, BruteSuppression, that can substantially reduce
the size of a rule set without negatively affecting performance, leading to a more compact, comprehensible model.
Finally, we propose three novel algorithms for unsupervised mining of approximately repeated patterns in time-series data, testing their performance in terms of
speed and accuracy on synthetic data, and on a real-world electricity-consumption device-disambiguation problem. We show that individual devices can be found automatically
and in an unsupervised manner using a local-shape-based approach
Associative Pattern Recognition for Biological Regulation Data
In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, we propose a series of associative pattern recognition algorithms in biological regulation studies. In particular, we emphasize efficiently recognizing associative patterns between genes, transcription factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels).
In protein-DNA associative pattern recognition, we introduce an efficient algorithm for affinity test by searching for over-represented DNA sequences using a hash function and modulo addition calculation. This substantially improves the efficiency of \textit{next generation sequencing} data analysis. In gene regulatory network inference, we propose a framework for refining weak networks based on transcription factor binding sites, thus improved the precision of predicted edges by up to 52%. In histone modification code analysis, we propose an approach to genome-wide combinatorial pattern recognition for histone code to function associative pattern recognition, and achieved improvement by up to . We also propose a novel shape based modification pattern analysis approach, using this to successfully predict sub-classes of genes in flowering-time category. We also propose a combination to combination associative pattern recognition, and achieved better performance compared against multi-label classification and bidirectional associative memory methods. Our proposed approaches recognize associative patterns from different types of data efficiently, and provides a useful toolbox for biological regulation analysis. This dissertation presents a road-map to associative patterns recognition at genome wide level
Accelerating Time Series Analysis via Processing using Non-Volatile Memories
Time Series Analysis (TSA) is a critical workload for consumer-facing
devices. Accelerating TSA is vital for many domains as it enables the
extraction of valuable information and predict future events. The
state-of-the-art algorithm in TSA is the subsequence Dynamic Time Warping
(sDTW) algorithm. However, sDTW's computation complexity increases
quadratically with the time series' length, resulting in two performance
implications. First, the amount of data parallelism available is significantly
higher than the small number of processing units enabled by commodity systems
(e.g., CPUs). Second, sDTW is bottlenecked by memory because it 1) has low
arithmetic intensity and 2) incurs a large memory footprint. To tackle these
two challenges, we leverage Processing-using-Memory (PuM) by performing in-situ
computation where data resides, using the memory cells. PuM provides a
promising solution to alleviate data movement bottlenecks and exposes immense
parallelism.
In this work, we present MATSA, the first MRAM-based Accelerator for Time
Series Analysis. The key idea is to exploit magneto-resistive memory crossbars
to enable energy-efficient and fast time series computation in memory. MATSA
provides the following key benefits: 1) it leverages high levels of parallelism
in the memory substrate by exploiting column-wise arithmetic operations, and 2)
it significantly reduces the data movement costs performing computation using
the memory cells. We evaluate three versions of MATSA to match the requirements
of different environments (e.g., embedded, desktop, or HPC computing) based on
MRAM technology trends. We perform a design space exploration and demonstrate
that our HPC version of MATSA can improve performance by 7.35x/6.15x/6.31x and
energy efficiency by 11.29x/4.21x/2.65x over server CPU, GPU and PNM
architectures, respectively
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
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