5,463 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
If only it were true: the problem with the four conditionals
The traditional division of conditionals into four main types (zero, first, second, and third) has long been called into question. Unfortunately, the awareness that this description does not reflect conditional patterns in actual usage has not generally been reflected in EFL coursebooks. This article re-examines the arguments for a description of conditional patterns which reflects actual usage and uses corpus data to demonstrate the kind of patterns in frequent use. It then suggests two teaching approaches that may help teachers to tackle a variety of conditional patterns in the classroom
Effective Literacy Instruction Strategies among Teachers in Elementary, Middle, and Secondary Grade Ranges
Many studies have been completed to identify the most Effective strategies used by successful teachers. Research has determined some of the most valuable classroom practices to increase student achievement in the areas of Reading and Writing. These studies and research tend to isolate grade levels and specific areas of Literacy Instruction to vocabulary, comprehension, phonics, phonemic awareness, fluency, or writing. Using the theoretical framework of Critical Theory and the instructional implications from John Dewey, Louise Rosenblatt, Paulo Freire, Lev Vygotsky, and M. M. Bahktin, this study proceeded with a concentration on Critical Literacy through student experiences, text interactions, cultural perspectives, individual interests, critical inquiry, and dialogue among students as well as texts. The purpose of this study was to identify instructional strategies and/or practices of Effective Literacy Teachers from multiple grade ranges. Once Effective teachers of literacy were identified by multiple quantitative and qualitative measures, interviews and observations were used to talk with teacher participants and identify specific methods of Literacy Instruction that were evident across Effective teachers of elementary, middle, and high school age ranges. Motivation and engagement of students, acknowledgement of student differences, and direct instruction of specific skills in literacy are all indicators of Effective instructional practices presented through research as well as denoted through observational and interview responses from teacher participants. Most of the participants indicated that they did not believe student success could be attributed to one strategy or a single instructional practice used regularly in their classrooms. They felt it was a combination of strategies that target student needs, experiences, and varying interest levels. When looking through the observations and interview responses, the variety and integration of strategies is supported by the frequency teachers discussed them as well as the numerous strategies observed in the classrooms. The teachers who participated in this study provided evidence of instructional strategies centered on student interests and lives from which to build meaningful opportunities and experiences that can help guide genuine learning
Glass transition and alpha-relaxation dynamics of thin films of labeled polystyrene
The glass transition temperature and relaxation dynamics of the segmental
motions of thin films of polystyrene labeled with a dye,
4-[N-ethyl-N-(hydroxyethyl)]amino-4-nitraozobenzene (Disperse Red 1, DR1) are
investigated using dielectric measurements. The dielectric relaxation strength
of the DR1-labeled polystyrene is approximately 65 times larger than that of
the unlabeled polystyrene above the glass transition, while there is almost no
difference between them below the glass transition. The glass transition
temperature of the DR1-labeled polystyrene can be determined as a crossover
temperature at which the temperature coefficient of the electric capacitance
changes from the value of the glassy state to that of the liquid state. The
glass transition temperature of the DR1-labeled polystyrene decreases with
decreasing film thickness in a reasonably similar manner to that of the
unlabeled polystyrene thin films. The dielectric relaxation spectrum of the
DR1-labeled polystyrene is also investigated. As thickness decreases, the
-relaxation time becomes smaller and the distribution of the
-relaxation times becomes broader. These results show that thin films
of DR1-labeled polystyrene are a suitable system for investigating confinement
effects of the glass transition dynamics using dielectric relaxation
spectroscopy.Comment: 10 pages, 11 figures, 2 Table
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
Spacings of Quarkonium Levels with the Same Principal Quantum Number
The spacings between bound-state levels of the Schr\"odinger equation with
the same principal quantum number but orbital angular momenta
differing by unity are found to be nearly equal for a wide range of power
potentials , with . Semiclassical approximations are in accord with this behavior. The
result is applied to estimates of masses for quarkonium levels which have not
yet been observed, including the 2P states and the 1D
states.Comment: 20 pages, latex, 3 uuencoded figures submitted separately (process
using psfig.sty
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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