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Fuzzy Time Series Method for Forecasting Taiwan Export Data

Author(s): P. Arumugam * , V. Anithakumari

Journal: International Journal of Engineering Trends and Technology
ISSN 2231-5381

Volume: 4;
Issue: 8;
Start page: 3342;
Date: 2013;
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Keywords: Time series | Stationary Stochastic Process | Fuzzy Time series model | ARIMA models | Sturges rule and Taiwan export.

Forecasting accuracy is one of the most favorable critical issues in Autoregressive Integrated Moving Average (ARIMA) models. The study compares the application of two forecasting methods on the amount of Taiwan export, the Fuzzy time series method and ARIMA method. Model discussed for the ARIMA method and Fuzzy time series method include the Sturges rules. When the sample period is extend in our models, the ARIMA models shows smaller than predicted error and closer predicted path to the realistic trend than those of the Fuzzy models, resulted in more accurate forecast of the export amount the Autoregressive Integrated Moving Average models. In the economic viewpoints, the amount of Taiwan export is mainly attributable to external factors. However, this impact reduces with time and export amount in the time series analysis. The ARIMA models can be utilized to predicted export value accurately, when all of value or data is available.
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