Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting (original) (raw)
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World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 2017
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A Comparative Study between Time Series and Neural Network for Exchange Rate Forecasting
Exchange rate forecasting has become a new research topic in present time for best market strategy, planning of investment for investor in foreign project and also for business profit. The methods or process which give the accurate forecasting result are not easy because there are several types of forecasting methods. Exchange Rate Forecasting is most important financial topic for individual investor, stock fund manager, financial analyst for their investment and stock market. Forecasting is to predict the future value of a particular field with the help of previous value or history. Various types of forecasting can be possible like exchange rate forecasting, stock market forecasting, gold price forecasting which affects the economy of a country. Now, there is a drastic change in Indian Rupee(INR) in terms of United State Dollar(USD) from 2009 to 2014. In exchange rate forecasting, there are various type of performance parameter on the basic of error by which we can consider which method gives the best forecasting result. These are mean square error, mean absolute error, root mean square error etc. Error can be calculate as the difference between the actual value and forecasted value. Neural Network gives the better result than time series method. Adaptive Neuro-Fuzzy Inference System is better than Time Series Method and Neural Network. Many factor affects the Indian Rupee which are Economic Activities, Interest Rate, Stock, Money Supply, In neural network, total data set can be divided into training and testing data and then train the dataset. According to the training, error can be calculated and compare with time series.
Exchange Rate Forecasting using ARIMA, NAR and ARIMA-ANN Hybrid Model
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In this paper we have studied the time serie of USD/ALL exchange rate. Based on the data of USD/ALL for the years 2000-2015, with monthly frequency, obtained by the Bank of Albania, we have done its modeling and forecasting using three types of methods: the autoregressive integrated moving average ARIMA, nonlinear autoregresive neural network (NAR) and the proposed hybrid method of ARIMA-ANN. As exchange rates are influenced by many political, economic and psychological factors, it is difficult to identify a unique economic model which can yield stable forecasts. However, we have used the univariate time series model, where is considered only the records of a single variable, exchange rate. We make the technical analysis, using the historical data to build the model to forecast future rate. The empirical analysis has shown very good results, mainly in the proposed hybrid model. The performance of the three methods was compared based on standard statistical measures. The ARIMA-ANN mo...
High order fuzzy time series for exchange rates forecasting
2011 3rd Conference on Data Mining and Optimization (DMO), 2011
Fuzzy time series model has been employed by many researchers in various forecasting activities such as university enrolment, temperature, direct tax collection and the most popular stock price forecasting. However exchange rate forecasting especially using high order fuzzy time series has been given less attention despite its huge contribution in business transactions. The paper aims to test the forecasting of US dollar (USD) against Malaysian Ringgit (MYR) exchange rates using high order fuzzy time series and check its accuracy. Twenty five data set of the exchange rates USD against MYR was tested to the seven-step of high fuzzy time series. The results show that higher order fuzzy time series yield very small errors thereby the model does produce a good forecasting tool for the exchange rates.
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Forecasting Exchange Rates using Time Series and Neural Network Approaches
Exchange rates play an important role in controlling dynamics of the foreign exchange market. Predicting exchange rates has become one of the most challenging applications of financial time series forecasting due to its unpredictability and volatility. This research study is to develop and compare the accuracy of two models; Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) as the time series model and Feedforward neural network with the Backpropagation algorithm as the Artificial Neural Network (ANN) model for predicting daily currency exchange rate of US Dollar against Sri Lankan Rupee (USD/LKR). For both models, past lagged observations of the data series and moving average technical indicators were employed as the explanatory variables and the predictive performance were evaluated using a number of widely used statistical metric. According to the performance of two models, it can be concluded that the ANN based model performs better when compared with the GARCH ...
A Fuzzy Model for Interval-Valued Time Series Modeling and Application in Exchange Rate Forecasting
Information Processing and Management of Uncertainty in Knowledge-Based Systems
Financial interval time series (ITS) is a time series whose value at each time step is an interval composed by the low and the high price of an asset. The low-high price range is related to the concept of volatility because it inherits intraday price variability. Accurate forecasting of price ranges is essential for derivative pricing, trading strategies, risk management, and portfolio allocation. This paper suggests a fuzzy rule-based approach to model and to forecast interval-valued time series. The model is a collection of functional fuzzy rules with affine consequents capable to express the nonlinear relationships encountered in interval-valued data. An application concerning one-step-ahead forecast of interval-valued EUR/USD exchange rate using actual data is also addressed. The forecast performance of the fuzzy rule-based model is compared to that of traditional econometric time series methods and alternative interval models employing statistical criteria for both, low and high exchange rate prices. The results show that fuzzy rule-based modeling approach developed in this paper outperforms the random walk, and other competitive approaches in out-of-sample interval-valued exchange rate forecasting.
HYBRID SYSTEM APPLICATION FOR TIME SERIES FORECASTING: THE CASE OF MYR/USD EXCHANGE RATE
Exchange rate prediction is a great interest and also is an important task, because, successful prediction of exchange rate may promise attractive benefits. This task is very difficult and highly complicated. In this paper, we investigate the predictability of exchange rate' return with a Neuro-Fuzzy system (as an intelligence system) that is a combination of neural networks and fuzzy inference system. The main purpose of this research is to determine whether a Neuro-Fuzzy system is capable to predict the exchange rate' return accurately. We attempt to model and predict the return on Malaysia Ringgit return (MYR/USD) with Neuro-Fuzzy system. The theory of relative price monetary model of exchange rate determination is used to determine the macro variables as inputs for the developed system. The experimental results reveal that the model successfully forecasts the monthly return of MYR/USD with a high accuracy. Furthermore, the constructed model easily outperformed the neural...