Abstract
Abstract Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield more accurate results. However, the forecasted interval of the FARIMA models may be very wide in some specific Circumstances. For instance, when data has high volatility or includes a significant difference or outliers. In this paper, a new hybrid model of FARIMA models is proposed by combining with probabilistic neural classifiers, called FARIMAH, in order to yield a more general and more accurate model than FARIMA models for financial forecasting in incomplete data situations. The main idea of the proposed model is based on this fact that the distribution of the actual values in the forecasted interval b...
Highlights
Time series forecasting is an important area of forecasting in which past observations of the same variable are analyzed in order to develop a model describing the underlying relationship
autoregressive integrated moving average (ARIMA) models have the advantage of accurate forecasting in a short time period and easy to implement, these models have some limitations that detract from their popularity for financial time series forecasting, such as data limitation
Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining several models or using hybrid models can be an effective way to improve forecasting performance
Summary
Time series forecasting is an important area of forecasting in which past observations of the same variable are analyzed in order to develop a model describing the underlying relationship. The model is used to extrapolate the time series into the future. This modeling approach is useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the dependent variable to other explanatory variables. One of the most popular and widely used time series models are autoregressive integrated moving average (ARIMA) models that have enjoyed fruitful applications in forecasting problems. ARIMA models have the advantage of accurate forecasting in a short time period and easy to implement, these models have some limitations that detract from their popularity for financial time series forecasting, such as data limitation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Intelligence Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
