Abstract
Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated namely ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.
Highlights
The financial domain is the most utilized environment for economic research aspects, making financial safety and security an important concern [1]
To implement the objectives of this study, investigate the daily exchange rate of the Euro against the Sudanese pound (SDG) in the Sudanese market, this data was collected from bank of Sudan, The data has a duration from the 3rd of July of 2016 to the 1st of December 2016
This paper submits a new hybrid model based on Artificial Neural Network (ANN), Exponential model (EXP), and ARIMA methods, which is constructed to predict SDG day closing prices agonist EURO
Summary
The financial domain is the most utilized environment for economic research aspects, making financial safety and security an important concern [1]. In trendy FTS, predicting, exchange ate have been recognized as one of the most difficult applications [4]. Several numbers of models are designed to support the stakeholders for intelligence precise predictions. The researchers proposed various conventional prediction models. Even so, traditional statistical models such as ARIMA, ARFIMA [5] ARMA, ARCH, GARCH, EXP, and AR those models unable to capture the complexness and behavior of the exchange rate [6]. Many researchers have introduced a lot of advanced nonlinear techniques as machine learning, including Artificial Neural Network (ANN) models [7], SVM algorithm, SVR methods [8], and data mining model like KNN algorithm [9]
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