Document Type

Report

Date

11-1990

Embargo Period

5-1-2012

Keywords

Neural networks, Multivariate time series, Autoregressive moving average models, Prediction

Language

English

Disciplines

Computer Sciences

Description/Abstract

This paper presents a neural network approach to multivariate time-series analysis. Real world observations of flour prices in three cities have been used as a benchmark in our experiments. Feedforward connectionist networks have been designed to model flour prices over the period from August 1972 to November 1980 for the cities of Buffalo, Minneapolis, and Kansas City. Remarkable success has been achieved in training the networks to learn the price curve for each of these cities, and thereby to make accurate price predictions. Our results show that the neural network approach leads to better predictions than the autoregressive moving average(ARMA) model of Tiao and Tsay [TiTs 89]. Our method is not problem-specific, and can be applied to other problems in the fields of dynamical system modeling, recognition, prediction and control.

Additional Information

School of Computer and Information Science, Syracuse University, SU-CIS-90-36

Source

local

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