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A Multi-Factor HMM-Based Forecasting Model for Fuzzy Time Series

Authors:
Hui-Chi Chuang
Wen-Shin Chang
Sheng-Tun Li

Keywords: fuzzy time series; forecasting; hidden Markov model (HMM).

Abstract:
In our daily life, people are often using forecasting techniques to predict weather, stock, economy and even some important Key Performance Indicator (KPI), and so forth. Therefore, forecasting methods have recently received increasing attention. In the last years, many researchers used fuzzy time series methods for forecasting because of their capability of dealing with vague data. The followers enhanced their study and proposed a stochastic hidden Markov model, which considers two factors. However, in forecasting problems, an event can be affected by many factors; if we consider more factors for prediction, we usually can get better forecasting results. In this paper, we present a multi-factor HMM-based forecasting model, which is enhanced by a stochastic hidden Markov model, and utilizes more factors to predict the future trend of data and get better forecasting accuracy rate.

Pages: 17 to 23

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-364-3

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014