An Adaptive Control Combination Forecasting Method for Time Series Data

Hongyan Jiang,Dianjun Fang,Xinyan Zhang,Samuel N Jator

doi:10.1155/2021/5573170

Abstract

According to the individual forecasting methods, an adaptive control combination forecasting (ACCF) method with adaptive weighting coefficients was proposed for short-term prediction of the time series data. The US population dataset, the American electric power dataset, and the vibration signal dataset in a hydraulic test rig were separately tested by using ACCF method, and then, the accuracy analysis of ACCF method was carried out in the study. The results showed that, in contrast to individual methods or combination methods, the proposed ACCF method was adaptive to adopt one or some of prediction methods and showed satisfactory forecasting results due to flexible adaptability and a high accuracy. It was also concluded that the higher the noise ratio of the tested datasets, the lower the prediction accuracy of the ACCF method; the ACCF method demonstrated a better prediction trend with good volatility and following quality under noisy data, as compared with other methods.

Highlights

A time series is a set of statistics and usually collected at regular intervals
Time series data are obtained by the sensors, and they refer to the large, diverse datasets of information that cannot be processed by using standard computers
Artificial intelligence models, subsuming BP neural network (BP-NET) [14, 15], support vector machines (SVM) [16], fuzzy logic models [17], and least square support vector machine (LSSVM) [18], have exhibited significant advantages in dealing with nonlinear problems. ese artificial intelligence models offered higher forecasting accuracy than physical or statistical models, but their prediction was mostly relying on training datasets, and they are easy to get stuck or suffer from overfitting in the local optima [19, 20]

Summary

Introduction

A time series is a set of statistics and usually collected at regular intervals. Time series data occur naturally in many application areas, such as economics, medicine, weather data, ocean engineering, finance, and engineering control. E three common methods for time series forecasting included physical, statistical, and artificial intelligence. Because of the inherent disadvantages of each model, nowadays, the effective information of multiple models has been used to predict time series, and weight problem of combination model is becoming the research focus. E two methods have better forecasting accuracy in only Dam’s settlement or landslip, but weights were assigned to all participating single models, and forecast accuracy in other time series cases was unwarrantable or unknown. The main problem of the above combination methods is that the statistical distribution information of the forecasting errors with the historical time is not paid more attention or is ignored, leading to unreasonable weight distribution and even negative weights.

Individual Methods

Steps of Computation

Results and Discussion

Accuracy Analysis of ACCF Method

MSEI SWA ACCF

Conclusions