A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Yanpeng Zhang,Hua Qu,Weipeng Wang,Jihong Zhao

doi:10.1155/2020/9546792

Abstract

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

Highlights

Prediction for future data based on analyzing temporal data is an important way to explore the value of data since a precise prediction is conductive to make policy analysis and decision in many fields, such as government [1], economics, and management [2]
Considering the instability of the data sources and the unreliability of data collecting process, most of the collecting data contain incomplete, imprecise, and ambiguous records, which makes preprocessing an indispensable procedure for machine learning. us, forecasting methods based on fuzzy time series have been proposed to cope with uncertainties caused by vagueness, ambiguity, and other nonprobabilistic reasons and widely applied in finance domain, such as Taiwan Stock
Many forecasting methods based on this framework were proposed. Most of these researches used an interval-based fuzzy time series (FTS) model to handle the fuzzification of the time series and applied fuzzy logic relationship, which can be executed on the FTS dataset, for making forecast

Summary

Introduction

Prediction for future data based on analyzing temporal data is an important way to explore the value of data since a precise prediction is conductive to make policy analysis and decision in many fields, such as government [1], economics, and management [2]. After high-order fuzzy time series was proposed, many works began to employ the linear model to make forecast. We proposed a new fuzzy time series forecasting model based on multiple linear regression and time series clustering. We proposed a new forecasting method based on fuzzy time series and the clustering algorithm. Us, we proposed a high-order MLRM-based time series clustering algorithm to give a suitable set of linear models. Denotes the number of samples in →X′. e regenerated high-order time series set has the same distribution of sample as the weighted high-order time series set and could be processed by the multiple linear regression model and ANN

A High-Order MLRM-Based Time Series Clustering

Update of the Distance Matrix and Classification of

A New Forecasting Model Based on Fuzzy Time Series and

Experimental Results and Analysis