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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Application of joint permutations for predicting coupled time series.

Eduardo Paucar Bravo¹, Kazuyuki Aihara¹, Yoshito Hirata²

¹Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Chaos (Woodbury, N.Y.)

|January 7, 2014

Summary

We present a novel model for multivariate time series prediction using joint permutations to partition state space. This approach extends previous methods and shows comparable performance for scalar time series forecasting.

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Area of Science:

Time Series Analysis
Machine Learning
Statistical Modeling

Background:

Existing models for time series prediction often struggle with multivariate data.
Previous work utilized permutations for scalar time series forecasting.

Purpose of the Study:

To introduce a new model for predicting multivariate time series data.
To extend the theoretical framework of permutation-based models to handle multivariate scenarios.
To compare the performance of the new multivariate model against the original scalar model.

Main Methods:

The proposed model partitions the state space using joint permutations.
A review of the theoretical underpinnings of permutation-based time series models was conducted.
The model's performance was evaluated by comparing predictions against a previously established permutation model for scalar data.

Main Results:

A novel model for multivariate time series prediction was successfully developed.
The model demonstrates a viable extension of permutation-based techniques to multivariate data.
Performance comparison indicates the model's effectiveness in multivariate time series forecasting.

Conclusions:

The introduced joint permutation model offers a promising approach for multivariate time series prediction.
This work extends the applicability of permutation-based methods to more complex, multivariate datasets.
Further research can explore advanced applications and optimizations of this multivariate forecasting technique.