Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
A Step-By-Step Implementation of a Hybrid USD/JPY Trading Agent
International Journal of Agent Technologies and Systems
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In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of training. Rather than using the last n lags for prediction, the input to the network is determined by the output of the PMRS (Pattern Modelling and Recognition System). PMRS matches current patterns in the time-series with historic data and generates input for the neural network that consists of both current and historic information. The results of the hybrid model are compared with those of neural networks and PMRS on their own. In general, there is no outright winner on all performance measures, however, the hybrid model is a better choice for certain types of data, or on certain error measures.