NDLI: A heuristic method for forecasting chaotic time series based on economic variables

Content Provider	IEEE Xplore Digital Library
Author	Reyhani, R. Moghadam, A.M.E.
Copyright Year	2011
Description	Author affiliation: Electrical and computer faculty, Islamic Azad University - Qazvin branch, Qazvin, Iran (Reyhani, R.; Moghadam, A.M.E.)
Abstract	Time series is one of the most attractive and mysterious mathematical subjects. Weather temperature, rainfall, water flow volume of a river and other similar cases in meteorology are known and predictable time series; amount of load peak, electricity price and other similar cases in electrical engineering are considerable time series. Time series forecasting is highly taken into account in economy. Stocks price in stock exchange market, currency equivalent rate in such market as Forex, world price of petroleum, sugar, gas, gold and other key stuffs are best known time series. The discovery of chaos in economics time such as stock exchange is highly regarded by scholars of economics. In recent years, chaos has proven in many economic time series such as stock changes. Also, it has been proven that discovery of chaos will help to forecast time series by intelligent algorithms better than before. In this paper, by propose a new heuristic method inspired from chaotic characteristic of economic time series, forecasts this time series by means of artificial neural networks. In proposed method, output of chaotic function is used to help time series prediction well.
Starting Page	300
Ending Page	304
File Size	249393
Page Count	5
File Format	PDF
ISBN	9781457715389
e-ISBN	9781457715396
DOI	10.1109/ICDIM.2011.6093338
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-26
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Economics Biological system modeling Time series analysis Chaos theory Heuristic methods Artificial neural networks Stock and currency value forecasting Time series Forecasting Biological neural networks
Content Type	Text
Resource Type	Article

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