Time series analysis

Tuesday, April 24, 2018 - 9:00am - 9:30am
While large climate model ensembles provide many insights, they also present a large burden in terms of computational resources and storage requirements. A complementary approach to large ensembles is to train statistical models on fewer runs. While far from capturing the complexity and high variable-dimensionality of climate model runs, simulations from a simpler statistical model might nevertheless provide insights on scientific questions of interest such as the variability of regional trends.
Thursday, January 15, 2009 - 8:30am - 9:15am
Oleg Prezhdo (University of Washington)
Device miniaturization requires an understanding of the dynamical response of materials on the nanometer scale. A great deal of experimental and theoretical work has been devoted to characterizing the excitation, charge, spin, and vibrational dynamics in a variety of novel materials, including carbon nanotubes, quantum dots, conducting polymers, inorganic semiconductors and molecular chromophores.
Monday, February 10, 2014 - 3:15pm - 4:05pm
Elizabeth Bradley (University of Colorado)
Most of the traditional time-series analysis techniques that are used
to study trajectories from nonlinear dynamical systems involve
state-space reconstructions and clever approximations of asymptotic
quantities, all in the context of finite and often noisy data. Few of
these techniques work well in the face of nonstationarity. Embedding
a time series that samples different dynamical systems at different
times, for instance---and then calculating a long-term Lyapunov
exponent---does not make sense.
Thursday, September 8, 2011 - 10:45am - 11:25am
Azadeh Moghtaderi (Queen's University)
In this talk, we will address two fundamental problems in time series analysis: The problem of filtering (or extracting) low-frequency trend, and the problem of interpolating missing data. We propose nonparametric techniques to solve these two problems. These techniques are based on the empirical mode decomposition (EMD), and accordingly they are named EMD trend filtering and EMD
Subscribe to RSS - Time series analysis