High Order Time Series Forecasting using Fuzzy Discretization

High Order Time Series Forecasting using Fuzzy Discretization

Mahua Bose, Kalyani Mali
Copyright: © 2016 |Pages: 18
DOI: 10.4018/IJFSA.2016100107
(Individual Articles)
No Current Special Offers


In recent years, various methods for forecasting fuzzy time series have been presented in different areas, such as stock price, enrollments, weather, production etc. It is observed that in most of the cases, static length of intervals/equal length of interval has been used. Length of the interval has significant role on forecasting accuracy. The objective of this present study is to incorporate the idea of fuzzy discretization into interval creation and examine the effect of positional information of elements within a group or interval to the forecast. This idea outperforms the existing high order forecast methods using fixed interval. Experiments are carried on three datasets including Lahi production data, enrollment data and rainfall data which deal with a lot of uncertainty.
Article Preview

2. Previous Works

Fuzzy set theory introduced by Lotfi A. Zadeh (1965, 1975), is based on the notion of partial containment. Fuzzy characteristic function is related to vagueness. In fuzzy logic degree of membership of a variable has a truth value that ranges between 0 and 1. The advantage of fuzzy forecasting is that it can handle problems using numerical as well as linguistic information such as low, moderate, high, very high, etc.

Among the earlier important research works on prediction were generated by Sugeno and Tanaka (1991)Wang and Mendel (1992). Song and Chissom (1993a, 1993b, 1994) developed the time variant and time invariant models for fuzzy time series forecasting. Further, many researchers, Chen (1996), Sullivan and Woodall (1994), Kim and Lee (1999), Chen and Hwang (2000), Huarng (2001), and Tsai and Wu (2001), worked on the development of various models of fuzzy time series forecasting and its implementations.

Complete Article List

Search this Journal:
Volume 13: 1 Issue (2024)
Volume 12: 1 Issue (2023)
Volume 11: 4 Issues (2022)
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing