Log In Sign Up

Predictive multiview embedding

by   M. LuValle, et al.

Multiview embedding is a way to model strange attractors that takes advantage of the way measurements are often made in real chaotic systems, using multidimensional measurements to make up for a lack of long timeseries. Predictive multiview embedding adapts this approach to the problem of predicting new values, and provides a natural framework for combining multiple sources of information such as natural measurements and computer model runs for potentially improved prediction. Here, using 18 month ahead prediction of monthly averages, we show how predictive multiview embedding can be combined with simple statistical approaches to explore predictability of four climate variables by a GCM, build prediction bounds, explore the local manifold structure of the attractor, and show that even though the GCM does not predict a particular variable well, a hybrid model combining information from the GCM and empirical data predicts that variable significantly better than the purely empirical model.


Local Prediction Pools

We propose local prediction pools as a method for combining the predicti...

Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine

Making accurate multi-step-ahead prediction for a complex system is a ch...

Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models

Variable importance is central to scientific studies, including the soci...

Using Twitter to predict football outcomes

Twitter has been proven to be a notable source for predictive modelling ...

A simple statistical approach to prediction in open high dimensional chaotic systems

Two recent papers on prediction of chaotic systems, one on multi-view em...

A statistical model of tristimulus measurements within and between OLED displays

We present an empirical model for noises in color measurements from OLED...

gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity

When faced with a supervised learning problem, we hope to have rich enou...