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

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

کلیدواژه ها:

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