activity
20132022
most citedModel-free quantification of time-series predictability

85 citations · 337 across the 15 of their papers we have counts for

collaborators

15 papers

physics.data-an2022★ 9 cited

Using scaling-region distributions to select embedding parameters

Varad Deshmukh, Robert Meikle, Elizabeth Bradley +2

Reconstructing state-space dynamics from scalar data using time-delay embedding requires choosing values for the delay and the dimension . Both parameters are critical to th…

astro-ph.SR2022★ 22 cited

Machine Learning Approaches to Solar-Flare Forecasting: Is Complex Better?

Varad Deshmukh, Srinivas Baskar, Elizabeth Bradley +2

Recently, there has been growing interest in the use of machine-learning methods for predicting solar flares. Initial efforts along these lines employed comparatively simple models…

physics.data-an2021★ 8 cited

Towards automated extraction and characterization of scaling regions in dynamical systems

Varad Deshmukh, Elizabeth Bradley, Joshua Garland +1

Scaling regions -- intervals on a graph where the dependent variable depends linearly on the independent variable -- abound in dynamical systems, notably in calculations of invaria…

cs.CY2021★ 1 cited

Computing Research for the Climate Crisis

Nadya Bliss, Elizabeth Bradley, Claire Monteleoni

Climate change is an existential threat to the United States and the world. Inevitably, computing will play a key role in mitigation, adaptation, and resilience in response to this…

cs.CY2020★ 10 cited

An Agenda for Disinformation Research

Nadya Bliss, Elizabeth Bradley, Joshua Garland +4

In the 21st Century information environment, adversarial actors use disinformation to manipulate public opinion. The distribution of false, misleading, or inaccurate information wi…

cs.IT2020★ 3 cited

Detection of Local Mixing in Time-Series Data Using Permutation Entropy

Michael Neuder, Elizabeth Bradley, Edward Dlugokencky +2

While it is tempting in experimental practice to seek as high a data rate as possible, oversampling can become an issue if one takes measurements too densely. These effects can tak…

physics.data-an2020★ 13 cited

Using Curvature to Select the Time Lag for Delay Reconstruction

Varad Deshmukh, Elizabeth Bradley, Joshua Garland +1

We propose a curvature-based approach for choosing good values for the time-delay parameter in delay reconstructions. The idea is based on the effects of the delay on the geome…

physics.data-an2018★ 47 cited

Anomaly Detection in Paleoclimate Records using Permutation Entropy

Joshua Garland, Tyler R. Jones, Michael Neuder +3

Permutation entropy techniques can be useful in identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using…

physics.geo-ph2018★ 5 cited

Climate entropy production recorded in a deep Antarctic ice core

Joshua Garland, Tyler R. Jones, Elizabeth Bradley +2

Paleoclimate records are extremely rich sources of information about the past history of the Earth system. Information theory, the branch of mathematics capable of quantifying the…

cs.IT2015★ 35 cited

A new method for choosing parameters in delay reconstruction-based forecast strategies

Joshua Garland, Ryan G. James, Elizabeth Bradley

Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series. The first step in this process is the estimation of good va…

math.DS2015★ 50 cited

Exploring the Topology of Dynamical Reconstructions

Joshua Garland, Elizabeth Bradley, James D. Meiss

Computing the state-space topology of a dynamical system from scalar data requires accurate reconstruction of those dynamics and construction of an appropriate simplicial complex f…

nlin.CD2015★ 31 cited

Prediction in Projection

Joshua Garland, Elizabeth Bradley

Prediction models that capture and use the structure of state-space dynamics can be very effective. In practice, however, one rarely has access to full information about that struc…

cs.IT2014★ 85 cited

Model-free quantification of time-series predictability

Joshua Garland, Ryan James, Elizabeth Bradley

This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-wo…

nlin.CD2013★ 4 cited

Determinism, Complexity, and Predictability in Computer Performance

Joshua Garland, Ryan James, Elizabeth Bradley

Computers are deterministic dynamical systems (CHAOS 19:033124, 2009). Among other things, that implies that one should be able to use deterministic forecast rules to predict their…

nlin.CD2013★ 14 cited

On the importance of nonlinear modeling in computer performance prediction

Joshua Garland, Elizabeth Bradley

Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This pap…