Overview and Comparison of Short-term Interval Models for Financial Time Series Forecasting

سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,042

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شناسه ملی سند علمی:

IIEC08_122

تاریخ نمایه سازی: 7 آذر 1391

چکیده مقاله:

In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models arefundamental to make decision and hence the research for improving the effectiveness of forecasting models have beencurried on. Many researchers have compared different timeseries models together in order to determine more efficient once in financial markets. In this paper, the performance offour interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressiveintegrated moving average (FARIMA), hybrid ANNs and fuzzy (F ANN) and Improved F ARIMA models are compared together. Empirical results of exchange rate forecastingindicate that the F ANN model is more satisfactory than other those models. Therefore, it can be a suitable alternativemodel for interval forecasting of financial time series

کلیدواژه ها:

Artificial Neural Networks (ANNs) ، AutoRegressive Integrated Moving Average (ARIMA) ، Time series forecasting ، Hybrid forecasts ، Interval models ، Exchange rate

نویسندگان

m Khashei

Isfahan University Of Technology

f Mokhatab Rafiei

Isfahan University Of Technology