The Application of Improved Elman Neural Network in the Exchange Rate Time Series

Hua, Tan

doi:10.1109/aici.2010.330

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…At the same time, with the further development of RNN network, the artificial intelligence trained by RNN and LSTM are not only applied to financial sector, but also to the UAV by Google, IBM, Jingdong and other enterprises. In addition, Bijari, Zhang, Tan et al applied a variety of variants of RNN to predict time series [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Based on ARIMA-RNN Combined Model

Yu¹,

Li²

2018

dtssehs

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Abstract:In this paper, we proposed a new hybrid ARIMA-RNN model to forecast stock price, the model based on moving average filter. This model can not only overcome the volatility problem of a single model, but also avoid the overfitting problem of neural network. We forecast stock price using ARIMA, RNN and ARIMA-RNN respectively, and we compare the value of MAE, MSE and MAPE of each model. We conclude that the hybrid ARIMA-RNN model has the best forecasting result.

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Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Based on ARIMA-RNN Combined Model

Yu¹,

Li²

2018

dtssehs

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Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

Nemes¹,

Butoi²

2013

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Predicting future prices by using time series forecasting models has become a relevant trading strategy for most stock market players. Intuition and speculation are no longer reliable as many new trading strategies based on artificial intelligence emerge. Data mining represents a good source of information, as it ensures data processing in a convenient manner. Neural networks are considered useful prediction models when designing forecasting strategies. In this paper we present a series of neural networks designed for stock exchange rates forecasting applied on three Romanian stocks traded on the Bucharest Stock Exchange (BSE). A multistep ahead strategy was used in order to predict short-time price fluctuations. Later, the findings of our study can be integrated with an intelligent multi-agent system model which uses data mining and data stream processing techniques for helping users in the decision making process of buying or selling stocks.

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The Application of Improved Elman Neural Network in the Exchange Rate Time Series

Cited by 2 publications

References 4 publications

Stock Price Prediction Based on ARIMA-RNN Combined Model

Stock Price Prediction Based on ARIMA-RNN Combined Model

Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

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