1,253 research outputs found
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
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
Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification
The variety of complex algorithmic approaches for tackling time-series
classification problems has grown considerably over the past decades, including
the development of sophisticated but challenging-to-interpret
deep-learning-based methods. But without comparison to simpler methods it can
be difficult to determine when such complexity is required to obtain strong
performance on a given problem. Here we evaluate the performance of an
extremely simple classification approach -- a linear classifier in the space of
two simple features that ignore the sequential ordering of the data: the mean
and standard deviation of time-series values. Across a large repository of 128
univariate time-series classification problems, this simple distributional
moment-based approach outperformed chance on 69 problems, and reached 100%
accuracy on two problems. With a neuroimaging time-series case study, we find
that a simple linear model based on the mean and standard deviation performs
better at classifying individuals with schizophrenia than a model that
additionally includes features of the time-series dynamics. Comparing the
performance of simple distributional features of a time series provides
important context for interpreting the performance of complex time-series
classification models, which may not always be required to obtain high
accuracy.Comment: 8 pages, 3 figure
Tracking the distance to criticality in systems with unknown noise
Many real-world systems undergo abrupt changes in dynamics as they move
across critical points, often with dramatic and irreversible consequences. Much
of the existing theory on identifying the time-series signatures of nearby
critical points -- such as increased signal variance and slower timescales --
is derived from analytically tractable systems, typically considering the case
of fixed, low-amplitude noise. However, real-world systems are often corrupted
by unknown levels of noise which can obscure these temporal signatures. Here we
aimed to develop noise-robust indicators of the distance to criticality (DTC)
for systems affected by dynamical noise in two cases: when the noise amplitude
is either fixed, or is unknown and variable across recordings. We present a
highly comparative approach to tackling this problem that compares the ability
of over 7000 candidate time-series features to track the DTC in the vicinity of
a supercritical Hopf bifurcation. Our method recapitulates existing theory in
the fixed-noise case, highlighting conventional time-series features that
accurately track the DTC. But in the variable-noise setting, where these
conventional indicators perform poorly, we highlight new types of
high-performing time-series features and show that their success is underpinned
by an ability to capture the shape of the invariant density (which depends on
both the DTC and the noise amplitude) relative to the spread of fast
fluctuations (which depends on the noise amplitude). We introduce a new
high-performing time-series statistic, termed the Rescaled Auto-Density (RAD),
that distils these two algorithmic components. Our results demonstrate that
large-scale algorithmic comparison can yield theoretical insights and motivate
new algorithms for solving important practical problems.Comment: The main paper comprises 18 pages, with 5 figures (.pdf). The
supplemental material comprises a single 4-page document with 1 figure
(.pdf), as well as 3 spreadsheet files (.xls
Highly comparative time-series analysis: The empirical structure of time series and their methods
The process of collecting and organizing sets of observations represents a
common theme throughout the history of science. However, despite the ubiquity
of scientists measuring, recording, and analyzing the dynamics of different
processes, an extensive organization of scientific time-series data and
analysis methods has never been performed. Addressing this, annotated
collections of over 35 000 real-world and model-generated time series and over
9000 time-series analysis algorithms are analyzed in this work. We introduce
reduced representations of both time series, in terms of their properties
measured by diverse scientific methods, and of time-series analysis methods, in
terms of their behaviour on empirical time series, and use them to organize
these interdisciplinary resources. This new approach to comparing across
diverse scientific data and methods allows us to organize time-series datasets
automatically according to their properties, retrieve alternatives to
particular analysis methods developed in other scientific disciplines, and
automate the selection of useful methods for time-series classification and
regression tasks. The broad scientific utility of these tools is demonstrated
on datasets of electroencephalograms, self-affine time series, heart beat
intervals, speech signals, and others, in each case contributing novel analysis
techniques to the existing literature. Highly comparative techniques that
compare across an interdisciplinary literature can thus be used to guide more
focused research in time-series analysis for applications across the scientific
disciplines
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A Physiologically Based Model of Orexinergic Stabilization of Sleep and Wake
The orexinergic neurons of the lateral hypothalamus (Orx) are essential for regulating sleep-wake dynamics, and their loss causes narcolepsy, a disorder characterized by severe instability of sleep and wake states. However, the mechanisms through which Orx stabilize sleep and wake are not well understood. In this work, an explanation of the stabilizing effects of Orx is presented using a quantitative model of important physiological connections between Orx and the sleep-wake switch. In addition to Orx and the sleep-wake switch, which is composed of mutually inhibitory wake-active monoaminergic neurons in brainstem and hypothalamus (MA) and the sleep-active ventrolateral preoptic neurons of the hypothalamus (VLPO), the model also includes the circadian and homeostatic sleep drives. It is shown that Orx stabilizes prolonged waking episodes via its excitatory input to MA and by relaying a circadian input to MA, thus sustaining MA firing activity during the circadian day. During sleep, both Orx and MA are inhibited by the VLPO, and the subsequent reduction in Orx input to the MA indirectly stabilizes sustained sleep episodes. Simulating a loss of Orx, the model produces dynamics resembling narcolepsy, including frequent transitions between states, reduced waking arousal levels, and a normal daily amount of total sleep. The model predicts a change in sleep timing with differences in orexin levels, with higher orexin levels delaying the normal sleep episode, suggesting that individual differences in Orx signaling may contribute to chronotype. Dynamics resembling sleep inertia also emerge from the model as a gradual sleep-to-wake transition on a timescale that varies with that of Orx dynamics. The quantitative, physiologically based model developed in this work thus provides a new explanation of how Orx stabilizes prolonged episodes of sleep and wake, and makes a range of experimentally testable predictions, including a role for Orx in chronotype and sleep inertia
Finding binaries from phase modulation of pulsating stars with \textit{Kepler}: VI. Orbits for 10 new binaries with mischaracterised primaries
Measuring phase modulation in pulsating stars has proved to be a highly
successful way of finding binary systems. The class of pulsating main-sequence
A and F variables known as delta Scuti stars are particularly good targets for
this, and the \textit{Kepler} sample of these has been almost fully exploited.
However, some \textit{Kepler} Scuti stars have incorrect temperatures
in stellar properties catalogues, and were missed in previous analyses. We used
an automated pulsation classification algorithm to find 93 new Scuti
pulsators among tens of thousands of F-type stars, which we then searched for
phase modulation attributable to binarity. We discovered 10 new binary systems
and calculated their orbital parameters, which we compared with those of
binaries previously discovered in the same way. The results suggest that some
of the new companions may be white dwarfs.Comment: 8 pages, 6 figures that make liberal use of colou
On the information-theoretic formulation of network participation
The participation coefficient is a widely used metric of the diversity of a
node's connections with respect to a modular partition of a network. An
information-theoretic formulation of this concept of connection diversity,
referred to here as participation entropy, has been introduced as the Shannon
entropy of the distribution of module labels across a node's connected
neighbors. While diversity metrics have been studied theoretically in other
literatures, including to index species diversity in ecology, many of these
results have not previously been applied to networks. Here we show that the
participation coefficient is a first-order approximation to participation
entropy and use the desirable additive properties of entropy to develop new
metrics of connection diversity with respect to multiple labelings of nodes in
a network, as joint and conditional participation entropies. The
information-theoretic formalism developed here allows new and more subtle types
of nodal connection patterns in complex networks to be studied
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