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

Differentiating Legislative from Nonlegislative Rules: An Empirical and Qualitative Analysis

The elusive distinction between legislative rules and nonlegislative rules has frustrated courts, motivated voluminous scholarly debate, and ushered in a flood of litigation against administrative agencies. In the absence of U.S. Supreme Court guidance on the proper demarcating line, circuit courts have adopted various tests to ascertain a rule’s proper classification. This Note analyzes all 241 cases in which a circuit court has used one or more of the enunciated tests to differentiate legislative from nonlegislative rules. These opinions come from every one of the thirteen circuits and span the period of the early 1950s through 2018. This Note identifies six different tests that courts have employed in this effort and offers a qualitative and empirical analysis of each. The qualitative analysis explains the underlying premise of the tests, articulates their merits and shortcomings, and considers how courts have applied them to particular disputes. The empirical portion of this Note uses regression analysis to ascertain how using or rejecting one or more of the tests affects a court’s determination of whether the rule is legislative or nonlegislative. This Note classifies the different tests into two categories: public-focused tests and agency-focused tests. These two categories are defined by a principle that permeates administrative law jurisprudence: achieving a proper balance between efficient agency rulemaking and maintaining a proper check against unconstrained agency action. These two categories thus defined, this Note proposes a balanced approach that incorporates elements of both categories to identify and refine the proper test

The CMB Derivatives of Planck's Beam Asymmetry

We investigate the anisotropy in cosmic microwave background Planck maps due to the coupling between its beam asymmetry and uneven scanning strategy. Introducing a pixel space estimator based on the temperature gradients, we find a highly significant (~20 \sigma) preference for these to point along ecliptic latitudes. We examine the scale dependence, morphology and foreground sensitivity of this anisotropy, as well as the capability of detailed Planck simulations to reproduce the effect, which is crucial for its removal, as we demonstrate in a search for the weak lensing signature of cosmic defects.Comment: 5 pages, 9 figures Published in MNRA

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