Clippers Tuesday: Cory Shain on Deconvolutional Time Series Regression

This talk proposes deconvolutional time series regression (DTSR) — a general-purpose regression technique for modeling sequential data in which effects can reasonably be assumed to be temporally diffuse — and applies it to discover temporal structure in three existing psycholinguistic datasets. DTSR borrows from digital signal processing by recasting time series modeling as temporal deconvolution. It thus learns latent impulse response functions (IRF) that mediate the temporal relationship between two signals: the independent variable(s) on the one hand and the dependent variable on the other. Synthetic experiments show that DTSR successfully recovers true latent IRF, and psycholinguistic experiments demonstrate (1) important patterns of temporal diffusion that have not previously been quantified in psycholinguistic reading time experiments, (2) the ability to provide evidence for the absence of temporal diffusion, and (3) comparable (or in some cases substantially improved) prediction quality in comparison to more heavily parameterized statistical models. DTSR can thus be used to detect the existence of temporal diffusion and (when it exists) determine data driven impulse response functions to control for it. This suggests that DTSR can be an important component of any analysis pipeline for time series.