Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach

Rout, Ajit Kumar; Dash, P.K.; Dash, Rajashree; Bisoi, Ranjeeta

doi:10.1016/j.jksuci.2015.06.002

Cited by 68 publications

(43 citation statements)

References 23 publications

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“…Particle swarm optimization (PSO) is a meta-heuristic global optimization method based on the movement of flocks of birds and fishes [12][13][14][15]. In PSO, each individual in swarm (particle) has a position referring to a possible solution in the search space.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Gold price prediction using an evolutionary pi-sigma neural network

Dash¹,

Routray²,

Rautray³

et al. 2018

IJET

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Gold Price Prediction has always been a fascinating area of study for researchers and decision makers who wish to determine its future value efficiently and accurately. In this study a predictor model is designed using a Pi-Sigma Neural Network (PSNN) for prediction of future gold price. Two evolutionary estimation paradigms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are suggested during training step of the Pi-Sigma Neural Network to optimize the tunable weights of the network. The model is evaluated based on certain performance evaluation criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) over two dataset such as Gold/INR and Gold/AED accumulated within the same period of time. The result analysis illustrates the better prediction

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Gold price prediction using an evolutionary pi-sigma neural network

Dash¹,

Routray²,

Rautray³

et al. 2018

IJET

Self Cite

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“…The key to predict the stock market is to fit a latent nonlinear relation between the history data and the future stock volatility. The traditional statistical models used for financial forecasting were [15] have an increasing popularity in this area.RNN is incorporated in our fundamental prediction model due to its appropriateness to address time series problem. The context layer stores the outputs of the state neurons from the previous time step and outputs to the next time step for computation.…”

Section: Stock Prediction With Recurrent Neural Networkmentioning

confidence: 99%

Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

Liu

Qin

et al. 2017

Lecture Notes in Computer Science

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In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.

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“…This features enabled NN to approximate well any nonlinear continuous functions [5]. But feedforward neural networks models suffer from slow convergence, local minimum, overfitting, have high computational cost and need a large number of iterations for its training due to the availability of hidden layer [6]. In black box system identification, however, the really important task is to build models for dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Also, their feedback properties (they have dynamic memories) make them dynamic and more efficient to represent nonlinear systems precisely which are essential for nonlinear forecasting and time-series estimation. RNNs have correctly modeled many of the Autoregressive Moving Average (ARMA) processes for nonlinear dynamical system identification [6].…”

Section: Introductionmentioning

confidence: 99%

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System Identification of Thermal Process using Elman Neural Networks with No Prior Knowledge of System Dynamics

Ibraheem¹

2017

IJCA

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Dynamic Neural networks have been verified as identifiers due to its capability for manipulating processes in parallel and enduring noisy sorts of the input signals. They make them outstanding contenders for system identification applications. This paper presents a method for a black box system identification based on Elman neural network (ENN) for thermal process system to generate a prototype for the dynamical system of the thermal process without any past information about the system dynamics. This identification approach is compared to its counterpart conventional feedforward neural network (CFFNN) based system identification. The comparative simulations show that the ERNN gives an excellent results and outperforms the CFFNN in terms of accuracy with little degradation in the speed of convergence which make this neural network a motivating candidate for adaptive and gain scheduling controllers. General Termssystem identification, Recurrent neural networks, thermal process, feedforward neural network, mean square error.

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Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach

Cited by 68 publications

References 23 publications

Gold price prediction using an evolutionary pi-sigma neural network

Gold price prediction using an evolutionary pi-sigma neural network

Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

System Identification of Thermal Process using Elman Neural Networks with No Prior Knowledge of System Dynamics

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