Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network

Rehman, Mehreen; Khan, Gul Muhammad; Mahmud, Sahibzada Ali

doi:10.1016/j.ieri.2014.09.083

Cited by 39 publications

(19 citation statements)

References 7 publications

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“…We have different currency exchange data so we have applied technical indicators for individual data. The technical indicators used here are mentioned in (10), (11), (12) and (13).…”

Section: Results Discussionmentioning

confidence: 99%

“…Generally fashionable ones are recurrent neural networks (RNNs), Fuzzy Logic,auto regressive moving average (ARMA),auto regressive integrated moving average (ARIMA),support vector machine (SVM),convolutional neural network (CNN), etc. [6][7][8][9][10][11]. The main intention of this work is forecasting of future currency exchange rate for INR with JPY and CNY data using a novel hybrid forecasting model LSTM with KNN which would be helpful for all economists and shareholders in the world.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Nayak,

Pavitra,

Tripathy

et al. 2019

IJEAT

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With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

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“…We have different currency exchange data so we have applied technical indicators for individual data. The technical indicators used here are mentioned in (10), (11), (12) and (13).…”

Section: Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Nayak,

Pavitra,

Tripathy

et al. 2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Besides, the most compatible way for comparison is to compare the level of confidence (i.e., accuracy percentage of perdition model) gained from applying the developed prediction models with the corresponding time-series. Therefore, Table I compares the proposed model results with eight other different prediction (i.e., forecasting) models reported in the literature, such as: ARIMA (AutoRegressive Integrated Moving Average) Model (Valipour, 2016;Parmar and Bhardwaj, 2014), Data-Driven Error Correction (DDEC) Model (Yan and Ouyang, 2019), Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model (Valipour, 2016), VECM (Vector Error Correction) Model (Jiang and Gong, 2014), RCG (Recurrent Cartesian Genetic) Model (Rehman et al, 2014) and Programming evolved Artificial Neural Network (PANN) model (Rehman et al, 2014). The comparison in the table is carried out in terms of: prediction technique and model order, time series length and level of accuracy.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

Al‐Haija¹,

Mao²,

Nasr³

2019

Journal of Computer Science

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In this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results showed that the optimal model order to estimate the given Facebook and Twitter time series are ARMA[5, 5] and ARMA[3, 3], respectively, since they correspond to the minimum acceptable prediction error values. Besides, the optimal models recorded a high-level of estimation accuracy with fit percents of 98.8% and 96.5% for Facebook and Twitter time series, respectively. Eventually, the developed framework can be used accurately to estimate the spectrum for any linear time series.

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“…When sequence has the higher self correlation, applying time series forecasting model to forecast will achieve good results. Durbin-Watson method is commonly used as inspection methods [4] [5]. Self correlation of data can be obtained using Matlab software [6].…”

Section: A Data Acquisition and Preprocessingmentioning

confidence: 99%

Prediction of Short Term Exchange Rate Using BP Neural Network

Liu¹

2017

Proceedings of the 2017 International Conference on Education Science and Economic Management (ICESEM 2017)

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Abstract-Exchange rate prediction accuracy is often about the survival of an enterprise or even national economic environment security. The effect of predicting the short-term exchange rate with general statistical method is not ideal. This paper proposed a multi-layer BP neural network model to predict short-term exchange rate. We conducted simulation experiments in Matlab 2010b, using data from China Merchants Bank foreign exchange market analysis software V2.8. The results show a better goodness of fit. It indicates that BP neural network can be of practical use in predicting short term exchange rate with high rate of accuracy.

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Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network

Cited by 39 publications

References 7 publications

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

Prediction of Short Term Exchange Rate Using BP Neural Network

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