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 $\alpha$-relaxation time becomes smaller and the distribution of the $\alpha$-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 $N$ but orbital angular momenta $\ell$ differing by unity are found to be nearly equal for a wide range of power potentials $V = \lambda r^\nu$, with $E_{N \ell} \approx F(\nu, N) - G(\nu,N) \ell$. 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 $c \bar c$ states and the 1D $b \bar b$ 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