Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Junjie Lu,Jinquan Huang,Feng Lu

doi:10.3390/app7030217

Abstract

A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.

Highlights

Time series prediction technology has already been studied over the past few decades, and a large amount of applications have been reported in a wide range of fields, such as weather forecasting [1], stock market prediction [2], communication signal processing [3], sales forecasting [4], and so on
Experiments on six time series prediction problems and a mechanical system identification problem have verified that the AWOS-Extreme learning machine (ELM) algorithm performs better in generalization performance, stability, and predictability than the online sequential extreme learning machine (OS-ELM) algorithm
For the purpose of having a fair comparison, the hidden nodes number of OS-ELM is optimized by the cross validation method, afterwards the optimized hidden nodes number is extended to the AWOS-ELM algorithm

Summary

Introduction

Time series prediction technology has already been studied over the past few decades, and a large amount of applications have been reported in a wide range of fields, such as weather forecasting [1], stock market prediction [2], communication signal processing [3], sales forecasting [4], and so on. The predicting accuracy of classical statistical methods suffer from the nonlinearity and complexity of many real time series. It has been verified that ELM costs much less training time and has better or similar generalization performance than SVM and traditional neural networks [17]. Experiments on six time series prediction problems and a mechanical system identification problem have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and predictability than the OS-ELM algorithm.

Preliminaries

Optimization Extreme Learning Machine

Online Sequential Extreme Learning Machine

The Proposed Adaptive Weight Online Sequential Extreme Learning Machine

Integrated Structure

Formula Derivation

Experiments

Benchmark Data Sets

Robot Arm Example

Conclusions